CHAPTER FIVE

PARTICLE-TO-FLUID MASS & HEAT TRANSFER Convective transport at low & high pressure. Free convection effects in Supercritical Fluids

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Fernando Pessoa (1888 – 1935)

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Chapter Five

One of the most important parameters needed in the design of packed bed systems is the particle-to-fluid transport coefficients. In the past six decades, a substantial amount of work has been devoted to the study of these parameters.

Particle-to-fluid mass transfer studies were first carried out by Gamson et al. (1943), and Hurt (1943). They obtained mass transfer coefficients from measurements of the rates of evaporation of water from wet porous particles. Hurt (1943) also reported mass transfer coefficients derived from the measurement of rates of naphthalene sublimation. Since their pioneering work, a large number of experimental studies have been carried out on mass transfer coefficients in packed bed systems. Theoretical work has also been in progress. Pfeffer (1964) and Pfeffer and Happel (1964) applied a free surface cell model to the creeping flow region. LeClair and Hamielec (1968, 1970) proposed a zero vorticity cell model, and El-Kaissy and Homsy (1973) applied the free surface cell model, zero vorticity cell model and distorted cell model to a multiparticle system at low Reynolds number. Nishimura and Ishii (1980) also applied the free surface cell model to the study of mass transfer at high Reynolds numbers. These models which are based on different assumptions, generally give different and inconsistent values of particle-to-fluid mass transfer coefficients. Therefore, theoretical prediction of mass transfer coefficients is far from satisfactory.

For the design and analysis of packed bed catalytic reactors, it is necessary to know the temperatures of the fluid and the catalyst particles in which the chemical reactions are taking place. In general, fluid temperature is measured with little difficulty, but the measurement of the solid surface temperature is not easy. This is particularly true of packed bed reactors. The particle temperature or temperature drop at the particle surface then has to be estimated in terms of the heat transfer coefficient between the particle and the fluid. Because of the importance of the particle-to-fluid heat transfer coefficient in packed bed reactors, a considerable effort has been made to evaluate this parameter. Experimental determinations of heat transfer coefficients for a wide variety of systems have been made using various experimental techniques, under either steady-state or unsteady-state conditions.

An extensive review on experimental/theoretical works on particle-to-fluid mass and heat transfer can be found in Wakao and Kaguei (1982).

Although there is broad information available of published experimental data for particle-to- fluid forced convection heat and mass transfer at low pressure (see e.g. Wakao and Kaguei, 1982), scarce data are available for high pressure situations or supercritical packed bed reactors. Many authors have studied catalytic reactions using a supercritical fluid as a solvent (Poliakoff et al., 1996; Ramirez et al., 2004), but main interest has been centered in kinetics of chemical reactions under supercritical conditions. Other authors have studied mass transfer in packed beds under supercritical conditions applied to supercritical extraction (Debenedetti and Reid, 1986; Stüber et al., 1996). The recent study and development of new compact packed bed supercritical nuclear reactors opens a branch for the study of heat transfer phenomena under high pressure conditions (see e.g. Oka et al., 1994).

In the following sections, analytical solutions of the steady-state temperature and composition profiles in a packed bed will be shown. In addition, a CFD simulation strategy for the estimation of forced convection mass and heat transfer coefficients at low pressure is discussed. Estimated transport parameters are compared to broadly accepted correlations. Mixed convection mass and heat transfer at high pressure is modeled and analyzed, and numerical results obtained are compared to previously published experimental data (Stüber et al., 1996), and a correlation for predicting heat transfer parameters in a supercritical packed bed reactor is presented (Guardo et al., 2006). The obtained numerical results validates the idea that the modified correlation presented by Guardo et al. (2006) can be used to describe heat transfer

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phenomena in a supercritical packed bed reactor under mixed convection regime at high pressures.

5.1. Geometry, mesh design and CFD modeling

To properly design a mesh capable of capturing the transport mechanisms present in the study in detail, a dimensionless analysis of Navier-Stokes equations under simulation conditions was developed. The dimensionless equations corresponding to continuity, mass, momentum, and energy balances are detailed in Chapter two. The orders of magnitude of the dimensionless groups were estimated by taking physical-chemical property values for air from experimental data and empirical correlations available in the literature (Reid et al., 1987; Yaws, 1999; Poling et al., 2000). Reynolds number was calculated using particle diameter as characteristic length. Reynolds analogy was used to estimate values of Prt from Ret (White, 1991). For first analysis purposes, Prt was assumed as a constant value within the bed, justified in the fact that majority of experimental results shows a range of variations between 0.75 < Prt < 2 for air and water (Kays, 1994). Boundary (operating) conditions for each analyzed situation are shown in Table 5.1. Details on the dimensionless numbers used can be found in Appendix C. Results of the order of magnitude of the dimensionless groups are shown in Table 5.2.

Boundary condition Low pressure High pressure

Mass transfer simulations

Circulating fluid CO2 3 C7H8 concentration at inlet, mol/m 0 C7H8 concentration at particle 5.95 120 – 190 surface (equilibria), mol/m3 Pressure, Pa 101325 9 – 9.2 x 106 Mass flow at the inlet, kg/m2· s - 0.015 – 0.100 Velocity at the inlet, m/s 7.5 x 10-4 – 7.5 x 10-1 - Heat transfer simulations

Circulating fluid Air CO2 Temperature at the inlet, K 298 330 Temperature at the particle surface, K 423 340 Pressure, Pa 101325 1 x 107 Mass flow at the inlet, kg/m2· s - 0.013 – 0.132 Velocity at the inlet, m/s 3 x 10-4 – 7.5 x 10-1 -

Table 5.1. Boundary (operating) conditions for analyzed cases

In the case of particle-to-fluid transport at low pressure, dimensionless analysis allows identifying the problem as forced convection in laminar or turbulent flow. For the momentum balance it becomes clear that viscous forces decrease their contribution as Re increases. Inertial gravity forces increase their contribution as Re decreases, and pressure drop together with turbulent forces become the most important terms in the momentum balance at high Re. In energy and species balances, the convective and the diffusive term become important for the balances. For both balances, steady state analysis can be used.

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When analyzing a mass/heat transfer case at high pressure, the problem is identified as mixed (free + forced) convection in laminar or transitional flow. Body forces and pressure drop become the most important terms in the momentum balance. Turbulence forces contribution to all balance equations is negligible. In order to couple species/energy and momentum balance, unsteady state analysis is required.

Low Pressure High Pressure

Re 100 101 102 103 100 101

Sr 102 101 100 10-1 105 104

Ma 10-6 10-5 10-4 10-3 10-6 10-5

Eu 103 - 104 101 - 103 10-1 - 101 10-1 106 104

Fr 10-4 10-4 - 10-2 10-2 - 100 100 10-10 10-8

Pr 10-1 10-1 10-1 10-1 100 100

Sc 100 100 100 100 100 100

Ec 10-10 10-10 - 10-8 10-8 - 10-6 10-6 10-16 10-14

ReT 101 100 100 100 101 100

PrT 10-1 10-1 10-1 10-1 10-1 10-1

ScT 103 102 101 100 103 102

Table 5.2. Dimensionless groups’ magnitude orders for analyzed cases

In order to define the computational domain, a 44 spheres stacking with a sphere-to-tube diameters ratio of 3.923 was chosen for the geometrical model. Modeled geometry was constructed following the bottom-up technique (generating surfaces and volumes from nodes and edges) in order to control mesh size around critical points (i.e. particle-to-particle and particle-to-wall contact points). In this study, to include real contact points, the spheres were modeled overlapping by 0.5 % of their diameters with the adjacent surfaces in the geometric model. For further details on the geometrical model please refer to Guardo et al., (2004) and Chapter Four of this thesis.

5.2. Model setup and analysis

Navier-Stokes equations together with species/energy balance were solved using commercially available finite volume code software Fluent® 5.x/6.x. For the low pressure models the fluid was taken to be incompressible, Newtonian, and in a laminar or turbulent flow regime. CO2, air and toluene at standard conditions were chosen as the simulation fluids. Incompressible ideal gas law for density, power law for viscosity and appropriated mixing rules were applied to the model for making these variables temperature and composition dependent. For the high pressure simulations, the fluid was taken to be Newtonian, in laminar flow regime and with variable density. CO2 and toluene were chosen in this case as the simulation fluids, and its

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properties at high pressure were incorporated to the solver code through User Defined Functions (UDF) and User Defined Equations (UDE). Peng-Robinson equation of state was used to calculate fluid’s density and heat capacity, Lucas method was used to calculate the viscosity, and thermal excess method was used to calculate the thermal conductivity (Reid et al., 1987). Appropriate mixing rules were applied to the model for making all variables composition- dependant. Details on the implementation of these UDE’s and UDF’s can be found in Appendix D.

Under-relaxation factors for pressure, momentum and energy were initially set to 0.05, 0.1 and 0.2, respectively (Gunjal et al., 2005), and increased progressively after convergence until values of 0.2, 0.4 and 0.8, respectively. In the case of turbulent flow simulations, under-relaxation factors for turbulent quantities were set in 0.4. A first order discretization scheme for pressure, momentum and energy equations was used until convergence was achieved, and the results obtained were used as initial solution for a new simulation applying a second order discretization scheme for momentum and energy equations.

Simulations were run in a Dell Precision 380 and Hewlett Packard Proliant DL385 workstations, and simulation times ranged from 1 to 240 hours depending on the studied case. Numerical convergence of the model was checked based on a suitable diminution of the normalized numerical residuals of all computed variables. For a more complete convergence checking the average static temperature or average species concentration at the bed outlet were also chosen as monitors depending on the case.

5.3. Results and discussion

5.3.1. Convective transport at low pressure

The main purpose of this set of simulations was to test CFD capabilities in a particle-to-fluid transport packed bed model. Standard correlations for Nu and Sh, and experimental data were selected as reference values to compare against the numerical results generated (Wakao et al., 1979, 1982). For each simulation, inlet velocity was varied (0.2 < Re < 1800), and the transport coefficients (Nu, Sh) were obtained. For the modeling in the turbulent flow region (Re > 300), previous experience shows that the Spalart-Allmaras turbulence model is a good choice (Guardo et al., 2005).

In packed beds with constant temperature or species concentration at particle surface, the transport resistance resides only on the fluid side. If axial dispersion is taken into consideration, then the balance equations for steady-state analysis are as shown in Table 5.3. For each simulation, variable profiles along the bed were recorded and analyzed. With the collected numerical results, the transport coefficients (kc, h) were obtained from Equations [5.3-1] and [5.3-2].

From the obtained values for kc and h, Sh and Nu were computed and compared with the broadly accepted correlations and experimental data. Results for mass transfer are shown in Figure 5.1. Results for heat transfer are shown in Figure 5.2. In both mass and heat transfer simulation sets, four meshes were used. It can be noticed that for both mass and heat transfer situations, in the laminar and transition flow zone (Re < 300) the results do not show dependence on the mesh density. The refinement of the mesh is expressed in terms of Vcell / Vp ratio.

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Mass balance Energy balance

2 2 dC 6kc ()1− ε d C dT f 6h ⋅ (1− ε ) d T f u + C − C = α ()f ps ax 2 u + ()T f − Tp = α ax 2 dx d p ⋅ε dx dx d p ⋅ε ⋅C p ⋅ ρ dx [5.3-1] [5.3-2] Boundary conditions

dC dT f u()C − C0 = α ax u()T − T = α dx [5.3-1a] f 0 ax dx [5.3-2a] at x = 0 (inlet) at x = 0 (inlet)

dC dT f = 0 = 0 dx [5.3-1b] dx [5.3-2b] at x = L (outlet) at x = xL (outlet)

Table 5.3. Mass and energy balance equations applied to the packed bed model

At lower Reynolds numbers (Re < 10), mass and heat transfer results obtained shows that the fitting against Wakao et al. correlations (1979, 1982) is not good. This is particularly noticeable in the case of the heat transfer simulations (see Figure 5.2). For a single velocity condition, different meshes give results in a wide range of Nu and no relation with mesh density can be established in any case. Experimentally, one-shot measurements have demonstrated that no definite Nu values can be obtained at low Re (Wakao, 1976). The fact that any Nu value within the obtained ranges yields approximately the same value of αax suggests that at this low Re, particle-to-fluid heat transfer makes little contribution to the overall heat transfer in the system, which analytically has been reflected in an increased confidence range for the selected correlation at low Re (Shent et al., 1981). Using a laminar model, a good accuracy for Sh and Nu values was obtained within 10 < Re < 100, reinforcing the idea that a similar methodology can be applied to model mixed convection heat transfer at high pressure in the same Re range. 100

10 Sh

Sc = 1.3 1 1 10 100 1000 10000 Re Serie10 -4 Serie11 -4 Serie12 -5 V cell / V p = 5.77 x 10 V cell / V p = 2.09 x 10 V cell / V p = 3.21 x 10 Vvcell/vp/ V = = 5.74 3.04 x 10-4x 10 -5 EXPERIMENTALExperimental dataDATA (Reviewed by Ranz & Marshall (1952) cell p Wakao et al.) Richardson & Szekely (1961) Wakao & Kaguei (1982)

Figure 5.1. Sherwood number vs. Reynolds number for the low pressure convective mass transfer packed bed model

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100

RANGE OF EXPERIMENTAL DATA (Reviewed by Wakao et al.)

10 Nu

Pr = 0.9 1 0.1 1 10 100 1000 Re 58933 elements -4 104325 elements -4 348065 elements -5 V cell /V p = 5.77 x 10 V cell /V p = 2.09 x 10 V cell /V p = 3.21 x 10 430051V /V elements = 3.04 x 10-5 Ranz & Marshall (1952) Wakao et al. (1979) cell p

Figure 5.2. Nusselt number vs. Reynolds number for the low pressure convective mass transfer packed bed model

For higher values of Re there is a divergence between the results obtained for tested meshes in both heat and mass transfer simulations, probably due to the fact that at higher Re, turbulent transport term in the transport equation becomes more important. In RANS modeling, the balance equations possess a smooth exact solution, and the numerical solution approaches that solution as we refine the grid. The aim of grid refinement is numerical accuracy (Spalart, 2000; Shur et al., 2005). Therefore, for our specific study case, an accurate turbulence modeling requires a denser mesh around the particles surface in order to capture the involved turbulence phenomena and its related mixing enhancement in the boundary layer (Guardo et al., 2005). The results obtained with the finer meshes fit better the prediction of Wakao et al. (1979) for heat transfer, and Wakao and Kaguei (1982) for mass transfer in the turbulent flow zone (Re > 300), probably due to a better capture of the vorticity energetic scales associated effects.

5.3.2. Convective transport at high pressure

A numerical modeling of a packed bed where mixed convection mass and heat transfer phenomena appear was also made. The same geometrical model as the aforementioned case was used but now the circulating fluid and the values of pressure and velocity were modified. The effects of density, flow rate and flow direction on mass and heat transfer are presented. Obtained numerical results for mass transfer were compared against the experimental results and the correlation presented by Stüber et al. (1996). Obtained numerical results for heat transfer were fitted, and a novel correlation based on the convective mass transfer correlation presented by Stüber et al. (1996) is proposed (Guardo et al., 2006a). An analogy between the obtained numerical data and previously reported heat transfer data (Guardo et al., 2006b) is also presented.

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Two simulation sets (upflow and downflow operation) were done, and for each simulation, inlet velocity was varied and Sh and Nu were obtained for 9 < Re < 100. A laminar flow model was used as viscous model for all simulations. Gravitational acceleration was added to the operating conditions of the model. Under-relaxation factors were set as previously explained for the aforementioned low pressure models. A first order discretization scheme for momentum and energy equations and a second order discretization scheme for pressure were used until convergence was achieved, and the results obtained were used as initial solution for a new simulation applying a second order discretization scheme for momentum and energy equations.

Equations [5.3-1] and [5.3-2] were used to obtain the values of Sh and Nu for each studied case (see Table 5.3). Boundary conditions for the model are described in Table 5.1. The presence of high density gradients lead to the formation of hydrodynamical instabilities (i.e., stream differentiation, countercurrent flow, recirculation) caused by buoyancy effects (Benneker et al., 1998). In order to compute correctly the flow fields and the heat transfer phenomena in these zones, the optimal grid used for the prior model was locally refined for each case studied around the zones where high density gradients were found.

Dimensionless analysis indicated that for the studied velocity range, laminar or transitional flow could be expected. For different mass and heat transfer simulations, local values of Gr, Pr, Sc and Re were calculated various points inside the boundary layer around the spheres in order to corroborate the transport mechanism present in the packed bed. Obtained values were compared with Metais-Eckert maps (Metais and Eckert, 1964) and are shown in Figure 5.3. As it can be observed, the main transport mechanism inside the packed bed under the different simulation conditions is the free convection in laminar/transitional flow regime.

1E+5 Forced Convection / Forced Turbulent flow 1E+4 Convection / Laminar flow Free Convection / Turbulent flow 1E+3 Re 1E+2

1E+1 Free Convection / Transition: Laminar flow Laminar - Turbulent

1E+0 1E+2 1E+3 1E+4 1E+5 1E+6 1E+7 1E+8 1E+9 Gr · (Pr or Sc) · (d/L)

Figure 5.3. Comparison between simulation data and Metais-Eckert maps

Churchill (1983) suggests that in a mixed convection heat transfer situation the transition 9 10 between laminar and turbulent flow can be found at 1 × 10 < GrH < 1 × 10 . In order to corroborate this idea, the numerical results obtained for Nu for the different heat transfer situations analyzed were plotted in a flow map according to Churchill’s criteria (see Figure 5.4).

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It can be noticed that the results obtained follow the expected behavior according to the dimensionless analysis, and transition between laminar and turbulent flow shows up in the results by means of a high increase in Nu for alike values of GrH.

20 Laminar FlowTransition Turbulent Flow

15

10 Nu

5

0 1E+7 1E+8 1E+9 1E+10 1E+11

Gr H

Assisting Flow Opposing Flow

Figure 5.4. Flow map according to Churchill’s (1983) criteria

5.3.2.1. Effect of density gradients and flow stability

Density gradients control the fluid movement at low velocities. Since the early experiments of Hill (1952), there has been a growth in the literature published on hydrodynamical instabilities due to density and viscosity variations in porous media (Homsy, 1987; Manickam and Homsy, 1995). Benneker et al. (1998) proved that the hydrodynamical instabilities caused by buoyancy effects in a packed bed when density increases with height lead to a significant increase in the flow axial dispersion within the bed.

While simulating the high pressure data sets, the presence of hydrodynamical instabilities captured by the model caused numerical instability in the results. The presence of large density gradients may result in convergence problems when solving the momentum, continuity and pressure equations (Lakshminarayana, 1991). As mentioned before, in order to minimize the effects of flow instability on calculations, the mesh was locally refined in each case trying to avoid the presence of high density and velocity gradients within a single cell. Numerical instability increased as inlet mass flow imposed as boundary condition decreased.

Figure 5.5 compares velocity fields (colored by density) for high and low pressure heat transfer situations. It can be seen that for the high pressure situation (Figure 5.5a) the presence of high density gradients (differences up to 40 kg/m3) lead to the differentiation of two streams: heavy (cold) fluid sinking into the bed, and light (hot) fluid floating out of the bed. This fact will be important when analyzing the effects of flow direction in free convection heat transfer. When analyzing the velocity–density profile for the low pressure situation (Figure 5.5b), it becomes

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clear that despite the fact a small density gradient is present in the results, flow field is controlled by the inlet velocity.

Figure 5.5. Velocity field (scale colored by density, kg/m3) in an axial cut of the packed 7 bed. (A) CO2 at 1 × 10 Pa and Re = 41; (B) Air at 101 325 Pa and Re = 531

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5.3.2.2. Effect of flow rate and flow direction

As mentioned before, flow instability zones within the bed appear if a density gradient is present along the bed. The direction of this density gradient and the direction of the flow have strong effects on the axial dispersion and the mixing of the flow (Benneker et al., 1998). Either if the flow direction assists the transfer phenomena (when density decreases with height) or is opposed to it (when density increases with height), it can be noticed that, under certain conditions, flow direction have a direct effect on mass and heat transfer phenomena. Several authors have reported in experiments that under supercritical conditions, flow direction affects extraction rates increasing them when flow direction assists the mass transfer (e.g. Sovová et al., 1994; Stüber et al., 1996; Saiz et al., 2000; Germain et al., 2005).

When gravity acceleration is added to the heat transfer model, the aforementioned phenomenology is captured by CFD analysis. Numerical runs were performed at various flow rates, constant pressure and temperatures. The fluid motion direction was also considered defining the fluid flowing in a direction opposite to gravity (upflow mode) or in its direction (downflow mode).

When analyzing mass transfer, for the case of extraction of toluene from the particles surface, Figure 5.6 presents a solute mass fraction contour plot within the packed bed, and Figures 5.7 and 5.8 illustrates the initial mass extraction rates for different flow rates and flow directions. From the figures, two conclusions can be drawn. (1) Regardless of flow direction, the extraction rate increases strongly with flow rate (see Figure 5.7); and (2) Despite the strong dependence on flow rate, gravity-assisting flow enhances the extraction rate significantly (see Figures 5.6 and 5.8). This effect appears to be more pronounced at low Re. This behavior is certainly related to the presence of free convection and has been experimentally noted by other authors (Sovová et al., 1994; Stüber et al., 1996).

Figure 5.6. Toluene mass fraction contour plot in an axial cut of the packed bed for [A] opposing flow and for [B] assisting flow with Re ≈ 20

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3.50E-05

3.00E-05

2.50E-05

2.00E-05

1.50E-05 extracted [kg]

8 1.00E-05 H 7

C 5.00E-06

0.00E+00 0 30 60 90 120 150 Time [s] Re = 13 Re = 20 Re = 36 Re = 78

Figure 5.7. Cumulative toluene extracted at the bed outlet vs. time for different Re in opposing flow regime

2.50E-05

2.00E-05

1.50E-05

1.00E-05 extracted [kg] 8 H

7 5.00E-06 C

0.00E+00 0 30 60 90 120 150 Time [s] Opposing flow Assisting flow

Figure 5.8. Cumulative toluene extracted at the bed outlet vs. time for different flow directions

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When analyzing heat transfer at high pressure, results obtained show a similar behavior than the aforementioned, reflecting that heat transfer coefficient values are significantly greater in assisting flow cases than those obtained for opposing flow cases (see figure 5.9). The heat transfer rising in assisting flow is favored by the temperature gradient direction (the same as flow direction), which increases flow mixing and heat transfer rates. The presence of an adverse density gradient (when temperature gradient direction is opposed to flow direction) diminishes heat transfer rates and lower coefficients are obtained.

20

16

12 Nu 8

4

0 40 50 60 70 80 90 100 Re Assisting flow Opposing flow

Figure 5.9. Nusselt number vs. Reynolds number for assisting and opposing flow simulations

In a similar way, flow velocity has a directly proportional effect over heat transfer when forced convection takes place. An increase on flow velocity leads to an increase on kinetic energy. This fact generates a better mixing and a greater heat transfer coefficient (see Figure 5.2). When analyzing a mixed convection case, the obtained results indicate that despite the fact that the forced convection component of the phenomena follows this behavior, free convection component is almost constant for all cases (see Figure 5.10). Is clear that free convection component is intrinsically related to density and temperature fields and it is not affected by velocity. Therefore, the dominant dimensionless group describing this phenomenon will be GrH rather than Re.

Independently of flow direction, for the covered range of Re the heat transfer coefficient value increases almost linearly with velocity (see Figure 5.9). This increase in the overall heat transfer is due exclusively to the increase of the contribution of forced convection to the total heat transfer rate.

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100

10 Nu

1 1 10 100 Re

Free Convection Forced Convection

Figure 5.10. Nusselt number vs. Reynolds number for free and forced convection in assisting flow

5.3.2.3. Validating the numerically obtained mass transfer data

Values of kc were evaluated from the mass balance equation (see Table 3), using numerical data and substance properties at corresponding operating conditions. From the obtained results for values of kc, Sh was computed and compared against experimental data and against the correlation presented by Stüber et al. (1996). The results of Sh vs. Re and the aforementioned comparison are given in Figure 5.11. As it can be seen, a good agreement between numerical results, experimental results and the correlation prediction is obtained.

100

10 Sh

1 10 100 Re CFD Experimental Stüber et al.

Figure 5.11. CFD obtained Sherwood number vs. Reynolds number for both upflow and downflow operations, and comparison against experimental data and correlations presented by Stüber et al. (1996).

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The contributions of free and forced convection to mass transfer could be evaluated following the indications of Stüber et al. (1996), finding also concordance between the numerically obtained results and the experimental results reported in that paper (see Figures 5.12 and 5.13).

100 (%)

/Sh free Sh

10 10 100 Re Opposing flow Assisting flow Experimental (Stüber et al.)

Figure 5.12. CFD obtained contribution of free convection to total mass transfer vs. Reynolds number and comparison against experimental data presented by Stüber et al. (1996).

100 0.3 )]/Sc 0 - Sh

free 10 ) ± (Sh 0

[(Sh - Sh 1 10 100 Re Opposing flow Assisting flow Experimental (Stüber et al.)

Figure 5.13. CFD obtained contribution of forced convection to total mass transfer vs. Reynolds number and comparison against experimental data presented by Stüber et al. (1996).

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5.3.2.4. Correlating the numerically obtained heat transfer data

Heat transfer coefficients at high pressure were obtained using a CFD simulation procedure similar to that applied for the mass transfer simulations presented in the prior sections of this chapter. The obtained numerical results validates the idea that a modified correlation based on the one proposed by Stüber et al. (1996) for mixed convection mass transfer under supercritical conditions in packed beds can be used to describe heat transfer phenomena in a supercritical packed bed reactor under mixed convection regime at high pressures. Such correlation can be useful when designing supercritical packed bed reactors, particularly in the design of direct- cycle supercritical-water-cooled fast nuclear reactors (Oka et al., 1994), nowadays under study and development process. Unfortunately, there is no experimental data available in order to validate the prior statement.

100 (%) Nu / free Nu

10 110100 Re Assisting Flow Opposing Flow

Figure 5.14. CFD obtained contribution of free convection to total heat transfer as a function of Reynolds number

The aforementioned idea of a high pressure mixed convection heat transfer correlation can be noticed explicitly in Figure 5.14 and Figure 5.15. Figure 5.14 shows the contribution of free convection to the total Nusselt number as a function of the Reynolds number. As expected, low Re numbers correspond to transport dominated by natural convection, but for Re around 100, still 30% of transport is due to natural convection (for both assisting and opposing flow). Figure 5.15 shows a reduction of the numerical data points to a natural convection-free basis only depending on the Re numbers. While the contribution of free convection to total heat transfer rate decreases with Re (as its value remains almost a constant), forced convection increases, leading to greater heat transfer coefficients.

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10.00 0.3 Pr )]/ 0 Nu - free 1.00 Nu ) ± ( ) ± 0 Nu - Nu [(

0.10 1 10 100 Re Assisting Flow Opposing Flow

Figure 5.15. CFD obtained contribution of forced convection to total heat transfer as a function of Reynolds number

After validating the numerical results obtained against experimental data and empirical correlations (see Figures 5.11, 5.12 and 5.13), an analogy between mass transfer data presented in this work (CFD and experimental) and numerical heat transfer data reported (Guardo et al., 2006) can be done, establishing a relationship between the selected data. It can be noticed that with a similar simulation methodology heat and mass transfer coefficients at high pressure can be obtained, because the CFD code applies the same mathematical procedure to estimate transport of any scalar quantity (such as mass or energy). Results obtained for heat and mass transfer simulations show a good fitting when compared to the prediction of the aforementioned correlations for heat and mass transfer. The parity plot for predicted vs. obtained Nusselt and Sherwood numbers is given in Figure 5.16. In summary, the correlations recommended are:

Nu − Nu0 = Nu forced m ()Nu free − Nu0 [5.3-3]

Taking Nu0 = 2 (Wakao and Kaguei, 1982), the correlations for free and forced convection are:

0.33 0.244 Nu free = Nu0 + 0.001()GrH ⋅ Pr Pr [5.3-3a]

0.88 0.3 Nu forced = 0.269Re Pr [5.4-3b]

Valid for the following ranges of dimensionless numbers:

9 < Re < 96; 2.2 < Pr < 3.3; 8 10 1 × 10 < GrH · Pr < 4 ×10 .

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An advantage of this correlation is that a single set of parameters predicts heat transfer for assisting flow free convection (with the minus sign in Equation [5.3-3]) as well as opposing flow free convection (with plus sign in Equation [5.3-3]). Predicted values for Nusselt number show that estimation error is within that obtained with original mass transfer correlation, that showed an AARD = 18.9% (see Figure 5.17).

20

15

10

5

Coefficient from CFD 0 0 5 10 15 20 Coefficient from correlations Heat transfer Mass transfer

Figure 5.16. Comparison of numerically obtained vs. predicted heat and mass transfer coefficients using the proposed correlations

25

20

15 T

Nu 10

5

0 1234567891011 Simulation run

CFD Correlation [Eq. 5.3-3]

Figure 5.17. Estimated error for the high pressure mixed convection correlation against simulation runs

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CONCLUSIONS

CFD proves to be a reliable tool when modeling convective mass and heat transfer phenomena in packed beds. It allows to analyze either free or forced convection situations and the obtained results can be compared qualitatively and quantitatively against previous published data.

Forced convection at low pressure in a packed bed was simulated, and the influence of velocity over mass and heat transfer could be analyzed. For values of Re > 10 it was obtained a good agreement with the correlations presented by Wakao and Kaguei (1982) for mass transfer, and Wakao et al. (1979) for heat transfer. At lower Reynolds numbers (Re < 10), results shows that the fitting against correlation is not good. This situation is especially notorious when analyzing heat transfer. For a single velocity condition different meshes give results in a wide range of Nu and no relation with mesh density can be established in any case. No mesh sensitivity was noticed for the laminar and transition flow zones, but in the turbulent flow zone a good definition of the mesh around the particles surface is of primal importance in order to capture the turbulence vorticity energetic scales associated effects.

Mixed (free+forced) convection at high pressure in a packed bed was also analyzed. For a supercritical fluid in laminar flow regime it was possible to study the effects of the density gradient, flow direction and velocity over mass and heat transfer. It was noticed that the presence of large density gradients conditioned the mesh influence over the numerical results when computing the mixing and the mass/heat transfer within the computed domain. An adverse density gradient generates hydrodynamical instabilities that produce an increase of the axial dispersion and a diminished mass/heat transfer rates. This fact caused numerical instability in the simulation process. In order to eliminate numerical instability, the mesh was locally refined in each case trying to avoid the presence of high density and velocity gradients along the bed.

Influence of flow direction over mass/heat transfer was also analyzed and it was noticed that in assisting flow regime, greater heat transfer rates are obtained. The rising in the density along the bed height due to the adverse density gradient helps the axial dispersion to grow, obtaining less mixing within the bed and smaller heat transfer coefficients.

Flow velocity also affects the heat transfer rate. The value of Sh and Nu increases almost linearly with flow velocity and this increase is due exclusively to the increase in the contribution of forced convection to the overall mass/heat transfer. Free convection is independent of flow velocity and its value remains almost constant within the studied velocity range.

A novel correlation (Eq. [5.3-3]) is presented for estimating the free and forced convection effects and Nu from Re, Pr and GrH . Presented correlations are valid for 9 < Re < 96, 2.2 < Pr < 8 10 3.3, and 1 × 10 < GrH · Pr < 4 × 10 . The obtained numerical results validates the idea that the modified correlation presented (Guardo et al., 2006), based on the one proposed by Stüber et al. (1996) for mixed convection mass transfer under supercritical conditions in packed beds, can be used to describe heat transfer phenomena in a supercritical packed bed reactor under mixed convection regime at high pressures.

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REFERENCES

Benneker, A., Kronberg, A., Westerterp, K., (1998). Influence of buoyancy forces on the flow of gases through packed beds at elevated pressures. AIChE Journal, 44, 263 – 270.

Churchill, S., (1983). Single phase convective heat transfer. In: Schlünder, E. et al. (Eds.): Heat Exchanger Design Handbook. Hemisphere publishing corporation, New York, NY, USA. (Sections 2.5.7 to 2.5.10)

Debenedetti, P., and Reid, R.C., (1986). Diffusion and mass transfer in supercritical fluids. AIChE Journal, 32, 2034 – 2046.

El-Kaissy, M.M., and Homsy, G.M., (1973). A theoretical study of pressure drop and transport in packed beds at intermediate Reynolds numbers. Industrial & Engineering Chemistry Fundamentals, 12, 82 – 90.

Gamson, B.W., Thodos, G., Hougen, O.A., (1943). Heat, mass and momentum transfer in the flow of gases through granular solids. Transactions of the American Institute of Chemical Engineers, 39, 1 – 35.

Germain, J., del Valle, J., de la Fuente, J., (2005). Natural convection retards supercritical CO2 extraction of essential oils and lipids from vegetable substrates. Industrial & Engineering Chemistry Research, 44, 2879 – 2886.

Guardo, A., Coussirat, M., Larrayoz, M.A., Recasens, F., Egusquiza, E., (2004). CFD Flow and heat transfer in nonregular packings for fixed bed equipment design. Industrial & Engineering Chemistry Research, 43, 7049 – 7056.

Guardo, A., Coussirat, M., Larrayoz, M.A., Recasens, F., Egusquiza, E., (2005). Influence of the turbulence model in CFD modeling of wall-to-fluid heat transfer in packed beds. Chemical Engineering Science, 60, 1733 – 1742.

Guardo, A., Coussirat, M., Recasens, F., Larrayoz, M.A., Escaler, X., (2006a). CFD study on particle-to-fluid heat transfer in fixed bed reactors: Convective heat transfer at low and high pressure. Chemical Engineering Science, 61, 4341 – 4353.

Guardo, A., Coussirat, M., Recasens, F., Larrayoz, M.A., Escaler, X., (2006b). CFD studies on particle-to-fluid mass and heat transfer in packed beds: free convection effects in supercritical fluids. Chemical Engineering Science (submitted – in revision).

Gunjal, P., Ranade, V., Chaudhari, R., (2005). Computational study of a single-phase flow in packed beds of spheres. AIChE Journal, 51, 365 – 378.

Hill, S., (1952). Channeling in packed columns. Chemical Engineering Science, 1, 247 – 253.

Homsy, G.M., (1987). Viscous fingering in porous media. Annual Reviews of Fluid Mechanics, 19, 271 – 311. Hurt, D.M., (1943). Principles of reactor design. Industrial & Engineering Chemistry, 35, 522 – 528.

Kays, W.M., (1994). Turbulent Prandtl number. Where are we? Journal of Heat Transfer, 116, 284 – 295.

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Lakshminarayana, B., (1991). An assessment of computational fluid dynamic techniques in the analysis and design of turbomachinery. The 1990 Freeman scholar lecture. Journal of Fluids Engineering, 113, 315–352.

LeClair, B.P., and Hamielec, A.E., (1968). Viscous flow through particle assemblages at intermediate Reynolds numbers. Steady-state solutions for flow through assemblages of spheres. Industrial & Engineering Chemistry Fundamentals, 7, 542 – 549.

LeClair, B.P., and Hamielec, A.E., (1970). Viscous flow through particle assemblages at intermediate Reynolds numbers. Steady-state solutions for flow through assemblages of cylinders. Industrial & Engineering Chemistry Fundamentals, 9, 608 – 613.

Manickam, O., and Homsy, G.M., (1995). Fingering instabilities in vertical miscible displacement flows through porous media. Journal of Fluid Mechanics, 288, 75 – 102.

Nishimura, Y., and Ishii, T., (1980). An analysis of transport phenomena for multi-solid particle systems at higher Reynolds numbers by a standard Karman—Pohlhausen method—I Mass transfer. Chemical Engineering Science, 35, 1205 – 1209.

Oka, Y., Koshizuka, S., Jevremovic, T., Okano, Y., (1994). Systems design of direct-cycle supercritical-water-cooled fast reactors. Nuclear Technology, 109, 1 – 10.

Pfeffer, R., (1964). Heat and mass transport in multiparticle systems. Industrial & Engineering Chemistry Fundamentals, 3, 380 – 383.

Pfeffer, R., and Happel, J., (1964). An analytical study of heat and mass transfer in multiparticle systems at low Reynolds numbers. AIChE Journal, 10, 605 – 611.

Poliakoff, M., George, M.W., Howdle, S.M., (1996). Inorganic and related chemical reactions in supercritical fluids. In: Van Eldik, R. (Ed.), Chemistry under Extreme and Non-classical Conditions. Spektrum, Heidelberg, pp. 189–218 (Chapter 5).

Poling, B., Prausnitz. J., O’Connell, J., (2000). The properties of gases and liquids. McGraw-Hill, New York. pp. 3.1 – 10.56.

Ramírez, E., Recasens, F., Fernández, M., Larrayoz, M.A., (2004). Sunflower oil hydrogenation on Pd/C in SC propane in a continuous recycle reactor. AIChE Journal, 50, 1545 – 1555.

Reid, R., Prausnitz, J., Poling, B., (1987). The properties of gases and liquids. McGraw-Hill, Boston. pp. 95 – 205.

Saiz, S., Larrayoz, M.A., Trabelsi, F., Recasens, F., (2000). Procedimiento para la extracción de lanolina de la lana y planta para la realización de dicho procedimiento. Spain patent No. P200002461.

Shent, J., Kaguei, S., Wakao, N., (1981). Measurements of particle-to-gas heat transfer coefficients from one-shot thermal responses in packed beds. Chemical Engineering Science, 36, 1283 – 1286. Shur, M., Spalart, P., Squires, K., Strelets, M., Travin, A., (2005). Three dimensionality in Reynolds-averaged Navier–Stokes solutions around two-dimensional geometries. AIAA Journal, 43, 1230 – 1242.

Sovová, H., Kučera, J., Jež, J., (1994). Rate of the vegetable oil extraction with supercritical CO2 – II. Extraction of grape oil. Chemical Engineering Science, 49, 415 – 420.

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Spalart, P.R., (2000). Strategies for turbulence modelling and simulations. International Journal of Heat and Fluid Flow, 21, 252 – 263.

Stüber, F., Vázquez, A.M., Larrayoz, M.A., Recasens, F., (1996). Supercritical fluid extraction of packed beds: external mass transfer in upflow and downflow operation. Industrial & Engineering Chemistry Research, 35, 3618–3628.

Wakao, N., (1976). Particle-to-fluid transfer coefficients and fluid diffusivities at low flow rate in packed beds. Chemical Engineering Science, 31, 1115 – 1122.

Wakao, N., Kaguei, S., Funazkri, T., (1979). Effect of fluid dispersion coefficients on particle-to- fluid heat transfer coefficients in packed beds: correlation of Nusselt numbers. Chemical Engineering Science, 34, 325 – 336.

Wakao, N., and Kaguei, S., (1982). Heat and mass transfer in packed beds. Gordon and Breach Science Publishers, New York. pp. 138 – 160; 264 – 295.

White, F., (1991). Viscous fluid flow. McGraw-Hill, New York. pp. 482 – 542.

Yaws, C., (1999). Chemical properties handbook. McGraw-Hill, New York. pp. 1 – 55.

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CHAPTER SIX

HIGH–PRESSURE HETEROGENEOUS REACTIONS CFD modeling on the supercritical hydrogenation of sunflower oil

« Verdrink niet in je eigen inspiratie en verbeelding; wordt geen slaaf van je eigen model »

Vincent van Gogh, « Het zonnebloemen » (1888)

Chapter Six

Partial hydrogenation of fats and oils is an important process in the food industry because not only increases oxidative stability of the final product when compared to raw materials, but also changes the physical characteristics of it, according to their final application (e.g. margarines or shortenings). In the conventional process, low reaction rates and the formation of undesirable by-products (such as trans fatty acids content about 40 wt%) are consequences of the low solubility of H2 in the oil and the high mass transfer resistance for the hydrogen in the liquid phase (Farrauto and Bartholomew, 1997). Supercritical technology has proven to be a reliable alternative to the conventional hydrogenation process (Härrod et al., 2001; Tacke et al., 2003) resulting increasing the rate of reaction and reduced trans isomer levels. It also provides a clean, economic and environmental friendly process.

Porous solid catalysts used for gas catalytic reactions have specific surface areas of tens to hundreds of square meters per gram. This enormous amount of surface area results mainly from the fine interconnecting pores in the catalyst pellets. If a chemical reaction is very fast, it proceeds at the external surface of the pellet. If, however, the reaction is very slow, the reactant gas may diffuse deep into the pores of pellet, even to the center of the pellet, and the chemical reaction takes place everywhere uniformly in the pellet.

In the laboratory, reaction rate determined directly by measurements using a differential reactor, for example, is the overall rate. The overall rate constant does not necessarily mean the intrinsic chemical reaction rate constant. For the design of industrial packed bed reactors, one needs to know the overall reaction rate, not the intrinsic chemical reaction rate. The overall rate is governed not only by the chemical reaction, but also by the diffusion rate through the pores inside the catalyst pellet as well as at the pellet’s external surface. If we simply measure activation energy from the overall reaction rate constants, the activation energy may differ from that of the intrinsic chemical reaction. The importance of diffusion is often underestimated by some catalyst chemists.

Wheeler (1951, 1955) made an extensive study on the role of pore diffusion in catalysis. Also, Petersen (1965), Smith (1970), Satterfield (1970), Jackson (1977), and Dullien (1979) have reviewed the subject of pore diffusion associated with chemical reaction well. The review article of Youngquist (1970) will also help readers understand the basic principles of diffusion and reaction in a porous catalyst.

In previous work done by our research group (Ramírez et al., 2004), an experimental design methodology and a response surface methodology were used to achieve optimum hydrogenation conditions for Pd catalyst in supercritical propane in a continuous well-mixed reactor. The results showed that it is possible to obtain a hydrogenated fat with 2 – 3 wt% trans C18:1 content and an iodine value (IV) of about 70 (starting from an initial value of 130). External resistance to mass transfer was made negligible using a high flow velocity over the catalyst bed (Re ≈ 6000). Furthermore, studies applying the Hashimoto et al. (1971) scheme provided information on the kinetics for the supercritical hydrogenation on a commercial-size Pd/C catalyst.

In later studies (Ramírez et al., 2006), the intra-particle diffusion mechanisms of the triglycerides and hydrogen in the hydrogenation were studied using supercritical propane for 2 % Pd on activated carbon. The method of measuring the effective diffusion coefficients was first to obtain the intrinsic kinetic constants on small-size catalyst particles (≈ 0.5 mm) in the absence of diffusional limitation and then using the intrinsic kinetic constants, to derive the diffusivities in diffusion-limited reaction runs using larger catalyst sizes (up to 2 mm). The results showed that while hydrogen is transported by bulk pore diffusion, the oil seem to diffuse by surface diffusion. Diffusivity for H2 is about 10 times greater than that for triglycerides.

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The aim of the work presented in this chapter was to apply Computational Fluid Dynamics (CFD) to the study of the catalytic hydrogenation of sunflower oil in the presence of a supercritical solvent. A 2D CFD model of a single Pd-based catalyst pellet is presented. Intra- particle concentration profiles for all species present in the mixture (oil triglycerides and hydrogen) are obtained and compared against experimental results. Different particle sizes are studied and external mass transfer and intra-particle diffusional effects are analyzed. Verified kinetic constants and intra-particle diffusion coefficients were fed into a 3D packed bed reactor model, and conversion profiles are obtained.

6.1. Geometry, mesh design and CFD modeling

To properly design a mesh capable of capturing the transport mechanisms present in the study in detail, a dimensionless analysis of Navier-Stokes equations under simulation conditions was developed. The dimensionless equations corresponding to continuity, mass, momentum, and energy balances are detailed in Chapter Two. The orders of magnitude of the dimensionless groups were estimated by taking physical-chemical property values for air from experimental data and empirical correlations available in the literature (Reid et al., 1987; Yaws, 1999; Poling et al., 2000). Reynolds number was calculated using particle diameter as characteristic length. Reynolds analogy was used to estimate values of Sct and Prt from Ret (White, 1991). Boundary (operating) conditions for each analyzed situation are shown in Table 6.1. Values for operating conditions were taken from experimental data published by Ramírez et al., (2006). Details on the dimensionless numbers used can be found in Appendix C. Results of the order of magnitude of the dimensionless groups are shown in Table 6.2.

Packed bed Boundary condition Single pellet model model

Circulating fluid Propane + H2 + Sunflower oil Mixture proportions (wt %) 95 : 4 : 1 Temperature, K 457 – 473 Pressure, Pa 2 x 107 Flow model Laminar; κ − ε Laminar Velocity at the inlet, m/s 0.13 – 1.32 1 x 10-3 – 2 x 10-2 Particle diameter, mm 0.10 0.47 0.92 2.00 0.47 L inlet concentration, 0.179669 0.217710 0.171589 37.73 mol/m3 O inlet concentration, 0.075731 0.094676 0.057084 13.32 mol/m3 E inlet concentration, 0.013177 0.018240 0.023614 0.00 mol/m3 S inlet concentration, 0.108529 0.056489 0.120502 2.57 mol/m3 H2 inlet concentration, 1.443499 1.5590 1.450727 242.28 mol/m3

Table 6.1. Boundary (operating) conditions for analyzed cases

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Magnitude order

Re 100 101 102 103 104

Sr 10-1 10-2 10-3 10-4 10-3

Ma 10-6 10-5 10-4 10-3 10-3

Eu 1010 108 106 104 104

Fr 10-2 10-1 100 101 100

Pr 100 100 100 100 100

Sc 10-1 10-1 10-1 10-1 10-1

Ec 10-10 10-8 10-6 10-4 10-4

ReT 10-3 10-2 10-1 100 101

PrT 103 103 103 103 103

ScT 102 102 102 102 102

Table 6.2. Dimensionless groups’ magnitude orders for analyzed cases

In the case of particle-to-fluid transport, dimensionless analysis allows identifying the problem as forced convection in laminar or turbulent flow. For the momentum balance it becomes clear that viscous forces decrease their contribution as Re increases. Inertial gravity forces increase their contribution as Re decreases, and pressure drop together with turbulent forces become the most important terms in the momentum balance at high Re. In energy and species balances, the convective and the diffusive term become important for the balances. For both balances, steady state analysis can be used.

In order to define the computational domains, 2 geometrical models were created. A 2D axisymmetric simplification of a single spherical catalyst pellet (see Figure 6.1) was built to study mass transfer and intra-particle diffusional effects. 4 different particle sizes were selected for this study (Dp = 0.1, 0.47, 0.9205 and 2 mm). A simple cubic sphere stacking (see Figure 6.2) was selected as the base unit cell in order to simulate a packed bed reactor. For the simulations the fluid was taken to be Newtonian, isothermal (457 - 473 K) and in a laminar (10 < Rep < 100 for the packed bed model) or turbulent flow regime (u = 1.3 m/s for the single sphere model). A mixture of propane, hydrogen and sunflower Figure 6.1. Mesh detail for the 2D single pellet oil (95 : 4 : 1) was chosen as the simulation geometrical model fluid.

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Figure 6.2. Mesh detail for the 3D packed bed unit cell geometrical model

6.2. Model analysis and setup

CFD has proven to be a reliable tool when modeling chemical reactors. Several reviews on the applicability of CFD methodology to chemical reactors design have been published in the last years (Bode, 1994; Harris et al., 1996; Kuipers and van Swaaij, 1998; Ranade, 2002). In this chapter, in order to study the transport and reaction mechanisms present in the supercritical hydrogenation of sunflower oil, Navier-Stokes equations together with the κ - ε turbulence model (when necessary) and a convection/diffusion mass balance were solved using a CFD commercially available finite element code software, Comsol Multiphysics 3.x. Simulations were run under isothermal (457 – 473 K) conditions. Steady state analysis was chosen for the simulations, following the guidelines obtained with the dimensionless analysis.

The kinetic and diffusional models proposed by our research group (Ramírez et al., 2004, 2006) for the supercritical hydrogenation of sunflower oil were also incorporated within the equations set of the CFD solver. A schematic representation of the kinetic model used is shown in Figure 6.3. Kinetic constants used for simulations can be seen in Table 6.3. Values of molecular and effective intra-particle diffusivities applied to the CFD model can be seen in Table 6.4. For further insight in the kinetic and diffusional model, please refer to Ramírez (2005).

Figure 6.3. Schematic representation of the kinetic model used for the supercritical hydrogenation of sunflower oil

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Temperature, K Parameter 457 473

k12 0.071 0.1 mol-0.5· m4.5· (kg Pd)-1· s-1 k13 1 x 10-6 1 x 10-6 mol-0.5· m4.5· (kg Pd)-1· s-1 k23 0.352 0.426 mol-0.5· m4.5· (kg Pd)-1· s-1 k32 0.603 0.933 mol-0.5· m4.5· (kg Pd)-1· s-1 k24 6.77 x 10-3 0.116 mol-1· m6· (kg Pd)-1· s-1 k34 7.13 x 10-3 0.02 mol-1· m6· (kg Pd)-1· s-1

Table 6.3. Fitted parameters values for the kinetic model

Particle 457 K 0.47 0.9205 2 diameter, mm Molecular 2.94 x 10-8 2.94 x 10-8 2.94 x 10-8 diffusivity, m2/s Oil Effective 2.94 x 10-8 1.76 x 10-8 8.82 x 10-9 diffusivity, m2/s Molecular 6.4 x 10-8 6.4 x 10-8 6.4 x 10-8 diffusivity, m2/s H2 Effective 6.4 x 10-8 3.84 x 10-8 1.92 x 10-8 diffusivity, m2/s

Particle 473 K 0.47 0.9205 2 diameter, mm Molecular 9.8 x 10-8 9.8 x 10-8 9.8 x 10-8 diffusivity, m2/s Oil Effective 9.8 x 10-8 5.88 x 10-8 2.94 x 10-8 diffusivity, m2/s Molecular 3 x 10-7 3 x 10-7 3 x 10-7 diffusivity, m2/s H2 Effective 3 x 10-7 1.8 x 10-7 9 x 10-8 diffusivity, m2/s

Table 6.4. Fitted molecular and effective diffusion coefficients for hydrogenation species on 2% Pd/C catalyst (Dp range = 0.47 - 2 mm)

Simulations were run in a Dell Precision 380 workstation, and simulation times ranged from 1 to 4 hours depending on the studied case. Numerical convergence of the model was checked based on a suitable diminution of the normalized numerical residuals of all computed variables. Concentration contour fields (see Figure 6.4) for all species present in the model were obtained and the numerical data recorded in order to analyze conversion, reaction velocities and mass transfer.

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Figure 6.4. (A) Concentration contour plot for Linoleic fatty acid (C18:2) in a single 2 % Pd catalyst pellet model (Dp = 0.9205 mm); (B) Concentration contour plots for fatty acids in a 3D packed bed model (Re = 100). Concentrations expressed in mol/m3

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6.3. RESULTS AND DISCUSSION

6.3.1. Validation of numerical results

In order to validate the numerical results obtained, a set of 2D simulations of particle-to-fluid mass transfer and reaction in a single catalyst pellet (emulating the experimental conditions proposed by Ramírez et al. (2006)) was made, and the results obtained were compared against the experimental results aforementioned. Intra-particle concentration profiles and intra-particle reaction rates were obtained for Dp = 0.47, 0.9205 and 2 mm, for which validation was possible due to the availability of experimental data.

First of all, it was necessary to verify the absence of gradients in the catalyst surface, since the experimental data was obtained in a Robinson-Mahoney gradientless reactor (Ramírez et al., 2004, 2006). For all particle diameters studied, concentrations (see Figure 6.5) and reaction velocity profiles (see Figure 6.6) along the surface arc were recorded. In all cases analyzed, flat variables profiles were obtained, assuring the absence of gradients along the catalyst surface.

0.24 1.6 ] ] 3 0.18 1.2 3 [mol/m [mol/m

0.12 0.8 , surface , 2

oil, surfaceoil, 0.06 0.4 H C C

Dp = 0.47 mm 0 0 -1.00 -0.50 0.00 0.50 1.00 Surface arc length [dimensionless] Linoleic Oleic Elaidic Stearic Hydrogen

Figure 6.5. Verification of the gradientless condition at the catalyst surface. Species concentration on catalyst surface for Dp = 0.47 mm

Once verified the aforementioned, intra-particle concentration profiles and intra-particle reaction rates were obtained and compared against experimental data previously published by our research group (Ramírez et al., 2006), obtaining for all cases a good agreement between the numerical and the experimental data (see Figures 6.7 and 6.8). Magnitude of relative errors was evaluated for the developed simulations, finding that this value was within the magnitude order of the experimental error previously reported by our research group. A trial for minimizing the relative error by means of optimizing the kinetic constants of the model and the effective diffusivities was also made, but an appreciable change was not observed in the concentration profiles obtained, remaining the magnitude order of the relative errors similar to those obtained when the original kinetic and diffusional model was used (Guardo et al., 2006).

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0.03 -0.08 ·s] ·s] 3 0.015 -0.11 3

[mol/m 0 -0.14 [mol/m

-0.015 -0.17 , surface 2 oil, surface H r r

Dp = 2 mm -0.03 -0.2 -1 -0.5 0 0.5 1 Surface arc length [dimensionless]

Linoleic Oleic Elaidic Stearic Hydrogen

Figure 6.6. Verification of the gradientless condition at the catalyst surface. Reaction rates on catalyst surface for Dp = 2 mm

0.25 1.6 1.4 0.2 1.2 ] ] 3 3 0.15 1 0.8 [mol/m [mol/m

0.1 2

oil oil 0.6 H C 0.4 C 0.05 0.2 Dp = 0.47 mm 0 0 -1 -0.5 0 0.5 1 Particle radius [dimensionless]

Linoleic Oleic Elaidic Stearic Linoleic - EXP Oleic - EXP Elaidic - EXP Stearic - EXP Hydrogen Hydrogen - EXP

Figure 6.7. Validation of numerical data obtained. Intra-particle species concentration profile for Dp = 0.47 mm. Experimental data taken from Ramírez et al. (2006)

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0.0045 -0.005

-0.01 0.003

-0.015 ·s] ·s] 3 0.0015 3 -0.02 [mol/m [mol/m

0 2 H oil -0.025 r r -0.0015 -0.03 Dp = 2 mm -0.003 -0.035 -1 -0.5 0 0.5 1 Particle radius [dimensionless] Linoleic Oleic Elaidic Stearic Linoleic - EXP Oleic - EXP Elaidic - EXP Stearic - EXP Hydrogen Hydrogen - EXP

Figure 6.8. Validation of numerical data obtained. Intra-particle reaction rates for Dp = 2 mm. Experimental data taken from Ramírez et al. (2006)

0.25 1.5

0.2 1.4

0.15 1.3 2 oil H C 0.1 1.2 C

0.05 1.1

0 1 0 0.2 0.4 0.6 0.8 1 Particle radius [dimensionless] Linoleic 0.1mm Oleic 0.1mm Elaidic 0.1mm Stearic 0.1mm Linoleic 0.47mm Oleic 0.47mm Elaidic 0.47mm Stearic 0.47mm Hydrogen 0.1mm Hydrogen 0.47mm

Figure 6.9. Comparison of the intra-particle concentration profiles for Dp = 0.1 mm (numerical) and Dp = 0.47 mm (experimental – Ramírez et al., 2006) in a single 2 % Pd catalyst pellet

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6.3.2. Verification of catalyst effectiveness at low particle sizes

Due to the difficulty for obtaining experimental data at very low particle sizes, a CFD simulation extrapolating the catalyst evaluation for Dp = 0.1 mm was also made in order to corroborate the catalyst effectiveness. It can be seen in Figure 6.9 that for a particle with Dp = 0.1 mm, numerically obtained concentration profiles were almost identical to those reported experimentally for Dp = 0.47 mm (Ramírez et al., 2006), reassuring the idea that for a particle diameter of 0.47 mm or lower, the effectiveness factor of the catalyst is close to the unity.

6.3.3. Particle-to-fluid mass transfer coefficients estimation

For each simulation, species concentration contour plots (Figure 6.4) were analyzed, and mass flux through the particle surface was recorded. With this data the surface local mass transfer coefficients (k) could be determined:

J i = ki ⋅ Ae ⋅ (Ci,surface − Ci,∞ ) [6.3-1]

Figure 6.10 shows the surface local mass transfer coefficients for oil and Figure 6.11 shows the surface local mass transfer for H2 obtained by means of the CFD simulations. It can be seen that the obtained mass transfer coefficient values are almost constant in the upstream surface sector, while in the downstream surface sector the mass transfer coefficient value is strongly diminished. This fact must be related with the formation of an stagnation/recirculation flow point downstream the catalyst pellet, affecting specially the mass fluxes over the catalyst surface.

0.05 0.10 mm

0.04

0.03 [m/s] 0.47 mm

0.02

oil, localoil, 0.9205 mm k

0.01 2 mm

0 -1.00 -0.50 0.00 0.50 1.00 Surface arc length [dimensionless]

Figure 6.10. Surface local mass transfer coefficients for oil fatty acids for the supercritical hydrogenation of sunflower oil at 20 MPa and 473 K

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0.09

0.07 0.10 mm

0.05

[m/s] 0.47 mm

, local 0.04 2

H 0.9205 mm k

0.02 2 mm

0.00 -1.00 -0.50 0.00 0.50 1.00 Surface arc length [dimensionless]

Figure 6.11. Surface local mass transfer coefficients for hydrogen for the supercritical hydrogenation of sunflower oil at 20 MPa and 473 K

From the values of k, the Sherwood number (Sh) was computed (see Table 6.5) and compared with the theoretical solution proposed by Ranz and Marshall (1952) for particle-to-fluid mass transfer in a single sphere (gas at low pressure), finding that obtained values for the Sherwood number are higher than those predicted by the aforementioned theoretical correlation (see Figure 6.12). When comparing the obtained results with an extrapolation for the correlation proposed by Tan et al. (1988) for supercritical extraction in packed beds or with an extrapolation of the experimental results obtained by Brunner (1985) for a high pressure bubble column, it can be noticed that the obtained values are lower than the extrapolation predictions. The absence of experimental data at such high Reynolds numbers difficult the validation tasks of the obtained numerical data. Nevertheless, the obtained numerical data suggests that the particle-to-fluid mass transfer coefficients for the supercritical hydrogenation of sunflower oil lie within the values for a gas (Ranz and Marshall, 1952) and a liquid (Brunner, 1985), which is coherent with the definition of a supercritical fluid (Guardo et al., 2006).

Sherwood number D , mm Re p L O E S H 0.10 647 44.16 44.16 44.18 44.18 27.61 0.47 3044 113.43 113.47 113.56 113.78 64.11 0.9205 5962 208.14 208.32 207.11 209.28 119.85 2.00 12954 628.94 621.55 622.79 620.35 360.41

Table 6.5. Computed values for Sherwood number for oil fatty acids and hydrogen in the supercritical hydrogenation of sunflower oil at 20 MPa and 473 K at different particle sizes

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100000

Brunner (1985) 10000

1000 THIS WORK Tan et al. (1988) Sh 100

10 Ranz & Marshall (1952) 1 10 100 1000 10000 100000 Re p

HYDROGEN OIL

Figure 6.12. Sherwood number vs. Reynolds number for different particle diameters on the supercritical hydrogenation of sunflower oil at 20 MPa and 473 K

6.3.4. Temperature effects on external mass transfer coefficients

In order to evaluate the temperature effects on the external mass transfer coefficients for the supercritical hydrogenation of sunflower oil, a simulation set at two different temperatures (457 and 473 K) was done. Particle size of Dp = 0.47 mm was chosen for the simulations because of being considered the optimal particle size for the hydrogenation reaction, as proven with the diffusional model and the intra-particle concentration profiles described in prior sections of this chapter. Values for the kinetic parameters and the values for the diffusion coefficients were set as show in Table 6.3 (for the kinetic model) and Table 6.4 (for the diffusional model).

From the numerical results obtained, surface local mass transfer coefficients (see Figure 6.13) and Sherwood numbers (see Table 6.6) for the temperatures selected were estimated, following a procedure similar to that explained priory on this chapter. It can be noticed that a temperature increase is reflected in a direct increase of the surface local mass transfer coefficients. When expressing these numerically obtained mass transfer coefficients in a dimensionless way, it can also be noticed that mass transfer coefficients increase with Reynolds.

Sherwood number Temperature, K Re L O E S H 457 3972 202.41 202.24 198.86 196.80 139.30 473 3044 113.43 113.47 113.56 113.78 64.11

Table 6.6. Computed values for Sherwood number for oil fatty acids and hydrogen in the supercritical hydrogenation of sunflower oil at different temperatures (particle size, Dp = 0.47 mm)

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0.03 0.045

473 K 0.0225 0.03375 H2

[m/s] OIL [m/s]

0.015 0.0225 , local 2 oil, local 457 K H2 H k 0.0075 0.01125 k OIL

Dp = 0.47 mm 0 0 -1.00 -0.50 0.00 0.50 1.00 Surface arc length [dimensionless]

Figure 6.13. Temperature effects over the surface local mass transfer coefficients for the supercritical hydrogenation of sunflower oil

6.3.5. Superficial velocity effects on external mass transfer coefficients

In order to evaluate the superficial velocity effects on the external mass transfer coefficients for the supercritical hydrogenation of sunflower oil, a simulation set at six different inlet flow velocities (u = 2.65, 1.32, 0.66, 0.132, 0.066 and 0.0132 m/s) was done. Particle size of Dp = 0.47 mm was chosen for the simulations because of being considered the optimal particle size for the hydrogenation reaction, as explained in prior sections of this chapter. Values for the kinetic parameters and the values for the diffusion coefficients were set as show in Table 6.3 (for the kinetic model) and Table 6.4 (for the diffusional model).

Superficial Sherwood number Re velocity, m/s L O E S H 0.0132715 30 8.75 8.75 8.75 8.75 5.73 0.0663575 152 19.54 19.54 19.54 19.54 12.72 0.1327150 304 30.40 30.24 30.43 30.33 17.54 0.6635750 1522 111.76 111.61 111.78 111.69 63.89 1.3271500 3044 113.43 113.47 113.56 113.78 64.11 2.6500000 6077 114.63 114.43 114.68 114.54 64.89

Table 6.7. Computed values for Sherwood number for oil fatty acids and hydrogen in the supercritical hydrogenation of sunflower oil at different inlet flow velocities (particle size, Dp = 0.47 mm)

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From the numerical results obtained, surface local mass transfer coefficients (see Figures 6.14 and 6.15) and Sherwood numbers (see Table 6.7) for the temperatures selected were estimated, following a procedure similar to that explained priory on this chapter.

0.045

0.03 [m/s]

local

, 0.015 H2 k

0 -1 -0.5 0 0.5 1 Surface arc length [dimensionless]

v = 2.65 m/s v = 1.32 m/s v = 0.66 m/s v = 0.13 m/s v = 0.066 m/s v = 0.013 m/s

Figure 6.14. Surface velocity effects on the hydrogen local mass transfer coefficients for the supercritical hydrogenation of sunflower oil.

0.025

0.02

0.015 [m/s]

0.01 oil, local oil, k 0.005

0 -1 -0.5 0 0.5 1 Surface arc length [dimensionless] v = 2.65 m/s v = 1,32 m/s v = 0,66 m/s v = 0,13 m/s v = 0.066 m/s v = 0.013 m/s

Figure 6.15. Surface velocity effects on the oil fatty acids local mass transfer coefficients for the supercritical hydrogenation of sunflower oil

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From Figures 6.14 and 6.15 and the numerical data shown in Table 6.7 it can be concluded that for high superficial velocities (u > 0.66 m/s), there is no significant effect of the superficial velocity over mass transfer phenomena. This behavior is in complete agree with the expected behavior for a well-stirred reactor. A similar situation has been studied experimentally by Ramírez et al., (2004) and Ramírez (2005), who reported for a CSTR supercritical hydrogenation reactor that an increase in operating stirrer speed (and in consequence, an increase in superficial velocity) from 52 rad/s to 262 rad/s produces no significant increase in the reaction rates of the hydrogenation.

When taking a look at the surface local mass transfer coefficients (shown in Figures 6.14, 6.15 and Table 6.7) at low superficial velocities (similar velocities to those used in supercritical packed bed reactors) it can be noticed that there is a clear effect of velocity over mass transfer. Obtained mass transfer coefficients are significantly lower than those obtained for a well-stirred reactor. It was noticed that surface reaction velocities decreased with surface velocity, and that the total mass flux over particle surface increased with the surface velocity. The diminution of the surface reaction rates and the increase of the surface mass fluxes can significantly affect the surface mass transfer coefficients.

6.3.6. Correlating the numerically obtained mass transfer data

Tan et al., (1988) correlated experimentally obtained mass transfer data for a fluid-solid system in a supercritical extractor, fitting their data with an equation of the type:

x y Sh = a ⋅ Re ⋅ Sc [6.3-2]

Following the aforementioned model, numerically obtained Sherwood numbers (shown in Tables 6.5, 6.6 and 6.7) were correlated as a function of Reynolds and Schmidt numbers, in order to present a simple set of equations capable to predict mass transfer coefficients in the supercritical hydrogenation of sunflower oil. Figure 6.16 shows the obtained correlations for the numerically obtained mass transfer data.

In summary, suggested correlations for predicting the oil fatty acids and hydrogen mass transfer coefficients in the supercritical hydrogenation of sunflower oil are as follows:

0.69 0.33 Shoil = 0.5192⋅ Re ⋅ Sc [6.3-3]

0.6765 0.33 Sh = 0.4796 ⋅ Re ⋅ Sc [6.3-4] H 2

Valid for the following range of dimensionless numbers:

30 < Re < 12954 0.7 < Sc < 2.1 AARD = 2.8 %

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1000

0.69 0.33 Sh oil = 0.5192 · Re · Sc [6.3-3] 100 Sh

10

0.6765 0.33 Sh H 2 = 0.4796 · Re · Sc [6.3-4]

1 1 10 100 1000 10000 100000 Re Oil Hydrogen Equation [6.3-3] Equation [6.3-4]

Figure 6.16. Correlations for the numerically obtained mass transfer data

6.3.7. Packed bed model

The validation of the intra-particle concentration profiles and the verification of the kinetic and diffusional models proposed by Ramírez et al. (2004, 2006) allowed to create a fully- heterogeneous 3D packed bed reactor model in order to study the supercritical hydrogenation process in a semi-industrial scale.

The packed bed model was created using a unit cell approach (a repetitive geometrical model) and simulations were carried by stages until full conversion was achieved for every case analyzed. Particle size of Dp = 0.47 mm was chosen for the simulations because of being considered the optimal particle size for the hydrogenation reaction, as explained in prior sections of this chapter. For every simulation stage, obtained contour fields for all computed variables (velocity and species concentration) were recorded at the model flow outlet, and fed as inlet boundary conditions for the following stage. This simulation strategy is highly time- demanding, but it guaranteed that the computational size of the CFD model was kept in reasonable sizes.

Figure 6.17 shows an example of the obtained concentration profiles at different simulation stages along the packed bed reactor. For all cases analyzed, it can be noticed that there is a fast conversion in the first sector of the packed bed reactor. This fact is related to the extremely high reaction rates achieved in this zone of the reactor, approximately 1000 times higher than for the traditional biphasic hydrogenation process (Tacke et al., 1996). This fact can be clearly noticed when analyzing the conversion profiles along the packed bed length (see Figure 6.18). The high reaction rates obtained at the bed inlet are reflected in the small reactor sizes obtained (less than 10 cm length for Re = 200).

- 132 - Chapter Six

Figure 6.17. Linoleic fatty acid concentration contour fields for different stages of the packed bed reactor model (T= 473 K; Re = 100)

150

100

50 Iodine Value (IV) Iodine

0 0 0.02 0.04 0.06 0.08 0.1 Reactor length [m]

Re = 10 Re = 40 Re = 70 Re = 100 Re = 150 Re = 200

Figure 6.18. Conversion profile along the packed bed length for different inlet velocities at 457 K

- 133 - Chapter Six

As it is important to minimize the presence of trans C18:1 (elaidic fatty acid) in the hydrogenation product due to its health implications (Ramírez, 2005), oil fatty acids mass fraction in the hydrogenated product was also obtained (see Figure 6.19). It can be seen that an increase in the reaction temperature makes the reaction move primarily to the formation of stearic fatty acid, reducing the formation of elaidic fatty acid. An increase in the reaction temperature produces an increase of the reaction rates, favoring the formation of stearic fatty acid.

0.6 L

0.4

0.2 O Mass fraction S E 0 120 100 80 60 Iodine value (IV)

E - 457 K L - 457 K O - 457 K S - 457 K E - 473 K L - 473 K O - 473 K S - 473 K

Figure 6.19. Oil fatty acids mass fraction in the hydrogenated product (Re = 200)

CONCLUSIONS

Computational Fluid Dynamics proves to be a useful tool when applied to the study of the catalytic hydrogenation of sunflower oil in the presence of a supercritical solvent. This computational technique allows to obtain in a fast and economical way information that otherwise is extremely complicated and expensive to obtain experimentally.

Numerical simulations for the supercritical hydrogenation of sunflower oil were validated against experimental data previously published by this research group (Ramírez et al., 2006), obtaining a good agreement in every case analyzed. Magnitude of relative errors was evaluated for the developed simulations, finding that this value was within the magnitude order of the experimental error previously reported by our research group.

Due to the difficulty for obtaining experimental data at very low particle sizes, a CFD simulation extrapolating the catalyst evaluation for Dp = 0.1 mm was also made in order to corroborate the catalyst effectiveness. It can be seen in Figure 6.9 that for a particle with Dp = 0.1 mm, numerically obtained concentration profiles were almost identical to those reported experimentally for Dp = 0.47 mm (Ramírez et al., 2006), reassuring the idea that for a particle diameter of 0.47 mm or lower, the effectiveness factor of the catalyst is close to the unity.

- 134 - Chapter Six

Obtained numerical data allowed analyzing local mass transfer coefficients over the catalyst surface. Sherwood number for the process was also evaluated, and it was found that its value lies in between those expected for a gas and those expected for a liquid.

Particle size, temperature and superficial velocity effects on external mass transfer were analyzed. It was found that mass transfer coefficients increased with an increase in temperature and in particle size. When analyzing the effects of the superficial velocity over the mass transfer coefficients, it was found that for high superficial velocities the system shows the behavior of a well-stirred reactor. It was observed that at high superficial velocities there is no effect over mass transfer coefficients.

Numerically obtained Sherwood numbers were correlated as a function of Reynolds and Schmidt numbers, in order to present a simple set of equations capable to predict mass transfer coefficients in the supercritical hydrogenation of sunflower oil. Correlations presented are valid for predicting mass transfer in a range of 30 < Re < 12954 and 0.7 < Sc < 2.1, whit an AARD = 2.8 %.

The validation of a single particle model allowed creating a 3D heterogeneous packed bed model in order to study the hydrogenation reaction in a semi-industrial scale, obtaining concentration and conversion profiles along the packed bed. Reaction rates obtained in the first 10 % of the length of the reactor are extremely high in every case analyzed (approximately 1000 times higher than for the traditional biphasic hydrogenation process). Further work must be done in the study of a packed bed reactor configuration in order to find optimal design and operation conditions for the industrial development of the supercritical hydrogenation process.

REFERENCES

Bode, J., (1994). Applications of computational fluid dynamics in the chemical industry. Chemical Engineering & Technology, 17, 145 – 148.

Brunner, G., (1985). Mass transfer in gas extraction, a supercritical fluid technology. In: J.M.L. Penninger, M. Radosz and M.A. McHugh (Eds.). Elsevier Science Publishers, Amsterdam.

Dullien, F.A.L., (1979). Porous media: Fluid transport and pore structure. Academic Press, New York.

Farrauto R.J., and Bartholomew C.H., (1997). Fundamentals of Industrial Catalytic Processes. London, UK: Chapman & Hall.

Guardo, A., Casanovas, M., Magaña, I., Martínez, D., Ramírez, E., Larrayoz, M.A., Recasens, F., (2006). CFD modeling on external mass transfer and intra-particle diffusional effects on the supercritical hydrogenation of sunflower oil. Chemical Engineering Science (submitted).

Harris, C.K., Roekaerts, D., Rosendal, F.J.J., Buitendijk, F.G.J., Daskopoulos, Ph., Vreenegoor, A.J.N., Wang, H., (1996). Computational fluid dynamics for chemical reactor engineering. Chemical Engineering Science, 51, 1569 – 1594.

Härröd, M., Van den Hark, S., Macher, M.B., Moller, P., (2001). Hydrogenation at supercritical single-phase conditions. In: Bertucco, A., and Vetter, G., (Eds.). High Pressure Process Technology: Fundamentals and Applications. Elsevier Science Publishers, New York.

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Hashimoto, K., Muroyama, K., Nagata, S., (1971). Kinetics of the hydrogenation of fatty oils. Journal of the America Oil Chemists’ Society, 48, 291 – 295.

Jackson, R., (1977). Transport in porous catalysts. Elsevier, New York.

Kuipers, J. A. M., and Van Swaaij, W. P. M., (1998). Computational fluid dynamics applied to chemical reaction engineering. In: Wei, J., Anderson, J., Bischoff, K., Denn, M., Seinfield, J., Stephanopoulos, G., (Eds.); Advances in Chemical Engineering 24; Academic Press: San Diego, CA; pp. 227 – 328.

Petersen, E.E., (1965). Chemical reaction analysis. Prentice-Hall, New Jersey.

Poling, B., Prausnitz. J., O’Connell, J., (2000). The properties of gases and liquids. McGraw-Hill, New York. pp. 3.1 – 10.56.

Ramírez, E., Recasens, F., Fernández, M., Larrayoz, M.A., (2004). Sunflower oil hydrogenation in Pd/C in SC propane in a continuous recycle reactor. AIChE Journal, 50, 1545 – 1555.

Ramírez, E., (2005). Contribution to the study of heterogeneous catalytic reactions in SCFs: Hydrogenation of sunflower oil in Pd catalysts at single-phase conditions. Ph.D. Thesis. Chemical Engineering Department, Universitat Politècnica de Catalunya, Barcelona.

Ramírez, E., Larrayoz, M.A., Recasens, F., (2006). Intraparticle diffusion mechanisms in SC sunflower oil hydrogenation on Pd. AIChE Journal, 52, 1539 – 1553.

Ranade, V., (2002). Computational flow modeling for chemical reactor engineering. Academic press, New York, pp. 244 – 422.

Ranz, W.E., and Marshall Jr., W.R., (1952). Evaporation from drops, part 1. Chemical Engineering Progress, 48, 173 – 180.

Reid, R., Prausnitz, J., Poling, B., (1987). The properties of gases and liquids. McGraw-Hill, Boston. pp. 95 – 205.

Satterfield, C.N., (1970). Mass transfer in heterogeneous catalysis, 2nd ed., McGraw-Hill, New York.

Tan, C.-S., Liang, S.-K., Liou, D.-C., (1988). Fluid-solid mass transfer in a supercritical fluid extractor. Chemical Engineering Journal, 38, 17 – 22.

Tacke, T., Wieland, S., Panster, P., (1996). Hardening of fats and oils. In: Rudolf von Rohr, P., and Trepp, Ch., (Eds.); Proceedings of the 3rd International Symposium on High-Pressure Chemical Engineering. Zurich, Switzerland. Elsevier Science Publishers; pp. 17 - 22.

Tacke, T., Wieland, S., Panster, P., (2003). Selective and complete hydrogenation of vegetable oils and free fatty acids in supercritical fluids. In: De Simone, J.M., (Ed.) Green Chemistry Using Liquid and Supercritical Carbon Dioxide. Oxford, UK: Oxford Univ. Press.

Wheeler, A., (1951). Advances in catalysis, Vol. 3. Academic Press, New York.

Wheeler, A., (1955). In: P.H. Emmett (Ed.), Catalysis, Vol. 2. Reinhold, New York.

White, F., (1991). Viscous fluid flow. McGraw-Hill, New York. pp. 482 – 542.

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Yaws, C., (1999). Chemical properties handbook. McGraw-Hill, New York. pp. 1 – 55.

Youngquist, G.R., (1970). SYMPOSIUM ON FLOW THROUGH POROUS MEDIA: Diffusion and flow of gases in porous solids. Industrial & Engineering Chemistry, 62 (8), 52 – 63.

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CHAPTER SEVEN

conclusions and future work

Conclusions

CFD proves to be a useful tool when modeling mass and heat transfer and chemical reactions in packed bed equipment. It allows obtaining velocity, pressure, temperature and species concentration contour plots inside the bed, reaching information almost impossible to get by means of experimental work.

Using CFD commercially available codes (a Finite Element code, Comsol 3.x, and a Finite Volume code, Fluent 5.x/6.x) allowed us to simulate and validate velocity and temperature profiles around simple geometries, and to study mass and heat transfer phenomena and heterogeneous reaction at low and high pressure in packed beds. A summary of the conclusions reached in each section of this thesis work is presented in this chapter.

7.1. Validation models

Two computational flow models were developed to validate flow and heat transfer around spheres (single or stacked).

For the flow validation test, flow around a simple cubic stack of spheres was used as validation model to test the capabilities of the solver reproducing experimental data. The model predictions were verified by comparing the simulation results with the published experimental and computational results. Predicted results showed excellent agreement with the experimental data of Suekane et al., (2003). Mesh sensitivity was established and optimal average mesh density for flow problems could be obtained.

For the heat transfer validation test, a sphere suspended in an infinite fluid was used as validation tool to test the capabilities of the solver reproducing an analytical solution. Drag coefficient over particle surface was recorded and compared against the Stokes’ law and the graphical correlation presented by Lapple and Shepherd (1940), obtaining an overall good agreement between the compared sets of data, and a neglectable mesh dependency on the results. In the case of the prediction of heat transfer parameters, mesh sensitivity tests were performed, and optimal average mesh density over the heat transfer surface was established. Numerical results obtained were compared against the theoretical answer for estimating the heat transfer coefficient obtained by Ranz and Marshall (1952), obtaining a good agreement between numerical and theoretical answers.

7.2. Wall-to-fluid heat transfer

CFD proves to be useful in the wall to fluid heat transfer parameter estimation, and also for calculation of pressure drop along the bed in packed bed equipment. It was possible to model a realistic case of a packed bed of spheres including contact points within the surfaces involved in the geometry. The calculated velocity profiles fitted qualitatively the expected results, and the calculated values of pressure drop along the bed adjust quite well with previously published and accepted correlations. Flow structures within the bed (i.e., wall channeling, stagnant points, eddy flows) were easily identifiable. Obtained temperature profiles inside the bed allowed estimating wall heat transfer parameters such as Nuw and kr/kf .

The results obtained for all of the cases studied (laminar and turbulent) agree among themselves and with the selected empirical and semi-empirical correlations when analyzing pressure drop and effective radial conductivity. This can be explained by the similarity in the

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velocity field obtained for each simulation. The calculation of these parameters is more closely related to velocity fields than to mixing parameters. The prediction of the mixing rate within the bed along with the near-wall treatment appreciably affects the estimate of Nuw. Flow regime zones can be identified using the heat transfer coefficient estimate. A laminar solution overestimates the value of this coefficient in the turbulent flow zone, and turbulent solutions tend to underestimate the value of the coefficient in the laminar transition zone. Turbulence models used for simulations do not predict the transition regime well, and this can be seen in the discrepancy between numerically obtained results and correlations in the low Re and transition range.

The definition of a good mesh allows calculations of fluid dynamics variables, as velocity and pressure. However, in our case, the proposed geometry governs mesh density and element size in the near-wall area, and this fact affects the definition of an appropriate y+ parameter in order to apply a correct near-wall treatment for certain turbulence models (e.g. the κ − ε family models). To define an adequate y+ for a correct coupling for the two-equation models at the near-wall treatment is not an easy task. Therefore, the y+ parameter is crucial during the selection of the appropriate turbulence model to apply within the simulation. A good near-wall modeling is fundamental to obtain more accurate results in pressure drop and heat transfer calculations and the selection of the right turbulent model will depend on the geometry proposed and the values obtained for y+ in the wall.

Results obtained with the Spalart–Allmaras turbulence model show better agreement than the two-equation RANS models for pressure drop and heat transfer parameter estimation. This could be due to the fact that this model uses a coupling between wall functions and damping functions for near-wall treatment, does not include additional diffusion or dissipation terms in its formulation and does not present the stagnation point anomaly. Factors such as the misestimating of ε, κ or µt can lead to differences in flow and temperature profiles that can be translated into miscalculation of heat transfer parameters. Therefore, the Spalart – Allmaras model could be a good tool for these kinds of flows because the y+ problem is solved automatically in spite of the necessity of checking the upper values of y+ (less than 120).

Turbulence models used for simulations do not properly predict the transition regime and this can be observed in the discrepancy between numerically obtained results and correlations in the low Re and transition range. Results present slower ratios of convergence at low Re than at high Re.

7.3. Particle-to-fluid convective mass and heat transfer at low and high pressure

CFD proves to be a reliable tool when modeling convective mass and heat transfer phenomena in packed beds. It allows to analyze either free or forced convection situations and the obtained results can be compared qualitatively and quantitatively against previous published data.

Forced convection at low pressure in a packed bed was simulated, and the influence of velocity over mass and heat transfer could be analyzed. For values of Re > 10 it was obtained a good agreement with the correlations presented by Wakao and Kaguei (1982) for mass transfer, and Wakao et al. (1979) for heat transfer. At lower Reynolds numbers (Re < 10), results shows that the fitting against correlation is not good. This situation is especially notorious when analyzing heat transfer. For a single velocity condition different meshes give results in a wide range of Nu and no relation with mesh density can be established in any case. No mesh sensitivity was

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noticed for the laminar and transition flow zones, but in the turbulent flow zone a good definition of the mesh around the particles surface is of primal importance in order to capture the turbulence vorticity energetic scales associated effects.

Mixed (free+forced) convection at high pressure in a packed bed was also analyzed. For a supercritical fluid in laminar flow regime it was possible to study the effects of the density gradient, flow direction and velocity over mass and heat transfer. It was noticed that the presence of large density gradients conditioned the mesh influence over the numerical results when computing the mixing and the mass/heat transfer within the computed domain. An adverse density gradient generates hydrodynamical instabilities that produce an increase of the axial dispersion and a diminished mass/heat transfer rates. This fact caused numerical instability in the simulation process. In order to eliminate numerical instability, the mesh was locally refined in each case trying to avoid the presence of high density and velocity gradients along the bed.

Influence of flow direction over mass/heat transfer was also analyzed and it was noticed that in assisting flow regime, greater heat transfer rates are obtained. The rising in the density along the bed height due to the adverse density gradient helps the axial dispersion to grow, obtaining less mixing within the bed and smaller heat transfer coefficients.

Flow velocity also affects the heat transfer rate. The value of Sh and Nu increases almost linearly with flow velocity and this increase is due exclusively to the increase in the contribution of forced convection to the overall mass/heat transfer. Free convection is independent of flow velocity and its value remains almost constant within the studied velocity range.

A novel correlation (Eq. [5.3-3]) is presented for estimating the free and forced convection effects and Nu from Re, Pr and GrH . Presented correlations are valid for 9 < Re < 96, 2.2 < Pr < 8 10 3.3, and 1 × 10 < GrH · Pr < 4 × 10 . The obtained numerical results validates the idea that the modified correlation presented (Guardo et al., 2006), based on the one proposed by Stüber et al. (1996) for mixed convection mass transfer under supercritical conditions in packed beds, can be used to describe heat transfer phenomena in a supercritical packed bed reactor under mixed convection regime at high pressures.

7.4. High pressure heterogeneous reaction

Computational Fluid Dynamics proves to be a useful tool when applied to the study of the catalytic hydrogenation of sunflower oil in the presence of a supercritical solvent. This computational technique allows to obtain in a fast and economical way information that otherwise is extremely complicated and expensive to obtain experimentally.

Numerical simulations for the supercritical hydrogenation of sunflower oil were validated against experimental data previously published by this research group (Ramírez et al., 2006), obtaining a good agreement in every case analyzed. Magnitude of relative errors was evaluated for the developed simulations, finding that this value was within the magnitude order of the experimental error previously reported by our research group.

Due to the difficulty for obtaining experimental data at very low particle sizes, a CFD simulation extrapolating the catalyst evaluation for Dp = 0.1 mm was also made in order to corroborate the catalyst effectiveness. It can be seen in Figure 6.9 that for a particle with Dp = 0.1 mm, numerically obtained concentration profiles were almost identical to those reported

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experimentally for Dp = 0.47 mm (Ramírez et al., 2006), reassuring the idea that for a particle diameter of 0.47 mm or lower, the effectiveness factor of the catalyst is close to the unity.

Obtained numerical data allowed analyzing local mass transfer coefficients over the catalyst surface. Sherwood number for the process was also evaluated, and it was found that its value lies in between those expected for a gas and those expected for a liquid.

Particle size, temperature and superficial velocity effects on external mass transfer were analyzed. It was found that mass transfer coefficients increased with an increase in temperature and in particle size. When analyzing the effects of the superficial velocity over the mass transfer coefficients, it was found that for high superficial velocities the system shows the behavior of a well-stirred reactor. It was observed that at high superficial velocities there is no effect over mass transfer coefficients.

Numerically obtained Sherwood numbers were correlated as a function of Reynolds and Schmidt numbers, in order to present a simple set of equations capable to predict mass transfer coefficients in the supercritical hydrogenation of sunflower oil. Correlations presented are valid for predicting mass transfer in a range of 30 < Re < 12954 and 0.7 < Sc < 2.1, whit an AARD = 2.8 %.

The validation of a single particle model allowed creating a 3D heterogeneous packed bed model in order to study the hydrogenation reaction in a semi-industrial scale, obtaining concentration and conversion profiles along the packed bed. Reaction rates obtained in the first 10 % of the length of the reactor are extremely high in every case analyzed (approximately 1000 times higher than for the traditional biphasic hydrogenation process). Further work must be done in the study of a packed bed reactor configuration in order to find optimal design and operation conditions for the industrial development of the supercritical hydrogenation process.

7.5. Future work

Further work on the study of heat and mass transfer phenomena in packed beds can be done applying a CFD methodology, especially when dealing with supercritical fluids. The advantages of CFD simulations (low cost, fast and reliable information) can be applied to situations where obtaining experimental data is technically complex and not cost effective. When dealing with supercritical fluids, materials and equipment needed to deal with the high pressures and high temperatures required for the experimentation are extremely expensive, limiting sometimes the experimentation possibilities. Further work can be done in this subject, using CFD to simulate complex situations involving supercritical fluids, as radial effects in packed beds or axial dispersion studies when natural convection is involved into the heat/mass transfer phenomena. Further research can be done for studying the effects of the configuration of the packing structure in the efficiency of the transport process, and the effects of the geometrical configuration (particle size, pore size and pore distribution) on transport and reaction parameters.

REFERENCES

Guardo, A., Coussirat, M., Recasens, F., Larrayoz, M.A., Escaler, X., (2006). CFD study on particle-to-fluid heat transfer in fixed bed reactors: Convective heat transfer at low and high pressure. Chemical Engineering Science, 61, 4341 – 4353.

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Lapple, C.E., and Shepherd, C.B., (1940). Calculation of particle trajectories. Industrial & Engineering Chemistry, 32, 605 – 617.

Ramírez, E., Larrayoz, M.A., Recasens, F., (2006). Intraparticle diffusion mechanisms in SC sunflower oil hydrogenation on Pd. AIChE Journal, 52, 1539 – 1553.

Ranz, W.E., and Marshall Jr., W.R., (1952). Evaporation from drops, part 1. Chemical Engineering Progress, 48, 173 – 180.

Suekane, T., Yokouchi, Y., Hirai, S., (2003). Inertial flow structures in a simple-packed bed of spheres. AIChE Journal, 49, 10 – 17.

Stüber, F., Vázquez, A.M., Larrayoz, M.A., Recasens, F., (1996). Supercritical fluid extraction of packed beds: external mass transfer in upflow and downflow operation. Industrial & Engineering Chemistry Research, 35, 3618–3628.

Wakao, N., Kaguei, S., Funazkri, T., (1979). Effect of fluid dispersion coefficients on particle-to- fluid heat transfer coefficients in packed beds: correlation of Nusselt numbers. Chemical Engineering Science, 34, 325 – 336.

Wakao, N., and Kaguei, S., (1982). Heat and mass transfer in packed beds. Gordon and Breach Science Publishers, New York. pp. 138 – 160; 264 – 295.

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APPENDICES

APPENDIX A

BIBLIOGRAPHIC REVIEW on CFD design issues, supercritical fluids extraction and reaction

Luke Andrews, « Open book »

Michael Graf, « Turbulence »

Appendix A

A-1. Extraction of natural products

A wide range of extractions of natural products from vegetal, animal and wood/fibre material can be carried out with a supercritical solvent (see Tables A-1 to A-3), and there is a growing interest in developing such processes, specially for the food, cosmetics and pharmaceutical industries.

T P Raw material Extract Solvent References [K] [M Pa] Doneanu and Angelica archangelica L. root Oil 313 12 CO2 Anitescu, 1998

Australian ginger root Oil 282 – 308 6 – 10 CO2 + ethanol Badalyan et al., 1998 Black pepper (Piper nigrun L.) Essential oil 303 – 323 15 – 30 CO2 Ferreira et al., 1999 CO2 + caprylic Borage (Borago officinalis L.) seeds Total extract 313 10 – 35 acid methyl Daukšas et al., 2002 ester Carvone, Baysal and Caraway (Carum carvi L.) seeds 305 – 348 7.5 – 30 CO2 Limonene Starmans, 1999 Coriander seed Oil 308 20 – 30 CO2 Illés et al., 2000

CO2 + Corn germ Oil 315 30 Rónyai et al., 1998 ethyl alcohol Cupuaçu (Theobroma CO2 de Azevedo Total extract 323 – 343 25 – 35 grandiflorum).,Brazilian fruit Ethane et al., 2003 β - Amyrin Dandelion leaves 308 – 338 15 – 45 CO2 Simándi et al., 2002 β - Sitosterol Oil Dehydrated orange peel 293 – 323 8 – 28 CO2 Mira et al., 1999 Limonene Dried Saw Palmetto berries 313 – 323 25 – 30 CO2 St. John’s Wort flowers 295 – 323 7 – 30 CO2 + ethanol Total extract Catchpole at al., 2002 Echinacea purpurea (aerial parts) 311 – 323 20 – 30 CO2 + ethanol Kava roots and stems 313 – 333 20 – 30 CO2 + ethanol Flowers of chamomile Total extract 303 – 313 10 – 20 CO2 Povh et al., 2001 (Chamomilla recutita [L.] Rauschert) Freeze dried carrots β - carotene 330 25 CO2 Subra et al., 1998 CO2 + ethanol Ginger (Zingiber officinale Roscoe) Oleoresin 303 – 313 20 – 25 CO2 + Zancan et al., 2002 propanol Terpene Ginkgo leaves lactones and 333 – 393 24 – 31 CO2 Chiu et al., 2002 flavonoids Ground pyrethrum flowers Pyrethrins 293 – 313 7 – 25 CO2 Kiriamiti et al., 2003

Guaraná seeds Caffeine 313 – 343 10 – 40 CO2 + H2O Saldaña et al., 2002 CO2 Hiprose fruit Total extract 298 – 308 5 – 25 Illés et al., 1997 Propane Jalapeño peppers (Capiscum Oleoresin 313 12 – 32 CO2 del Valle et al., 2003 annuum L.) prepelletised Lemon balm (Melissa Total extract 308 – 313 10 – 18 CO2 Ribeiro et al., 2001 Officinalis L.) Antioxidants

Lemongrass (Cymbopogon citratus) Essential oil 296 – 323 8.5 – 12 CO2 Carlson et al., 2001 Lovage (Levisticum officinale Koch.) Essential oil 313 – 323 8 – 35 CO2 Daukšas et al., 1999 Nutmeg (Myristica fragans Oil 296 9 CO2 Spricigo et al., 1999 Houttuyn) CO2 Pungent spice paprika powder Oleoresin 308 – 328 10 – 40 Daood et al., 2002 Propane Shells of the bacuri fruit (Platonia Total extract 289 – 323 6.3 – 20 CO2 + ethanol Monteiro et al., 1997 insignis Mart)

Soybeans Oil 333 – 353 16 – 69 CO2 + ethanol Montanari et al., 1999 Summer savory (Satureja hortensis Oil 313 12 – 18 CO2 Esquível et al., 1999 L.)

Table A-1. Vegetal products extractions carried out in SCF

- 151 - Appendix A

T P Raw material Extract Solvent References [K] [M Pa] Atlantic mackerel (Scomber Oil 308 34.5 CO2 Dunford et al., 1998 scombrus) PCB, Black scabbardfish (Aphanopus chlorinated 309 – 337 10 - 24 CO2 Antunes et al., 2003 carbo) pesticides Vedaraman Cow brain Cholesterol 323 - 343 23 – 27 CO2 et al., 2005 Boselli and Dried egg yolk Phospolipids 313 51.7 CO2 Caboni, 2000 Pigskin Fat 293 – 313 10 - 60 CO2 Vaquero et al., 2006 Raw wool Natural wax 303 - 353 7 - 20 CO2 + ethanol Eychenne et al., 2001 318 10 – 24 Marsal et al., 2000a Sheepskin Fat CO2 313 10.4 – 24 Marsal et al, 2000b López-Mesas Wool scour wastes Wax 353 25 CO2 + toluene et al., 2005

Table A-2. Fish and animal material extractions carried out in SCF

T P Raw material Extract Solvent References [K] [M Pa]

CO2 + methanol, acetyl acetone, Cellulose-based filter papers Uranyl nitrate 333 25 methylisobutyl Shamsipur et al., 2001 ketone, Tributyl phosphate

Sugar cane bagasse CO2 + Lignine 415 – 471 14.7 - 23 Pasquini et al., 2005 Pinus Taeda wood chips ethanol/water Wood pulp Total extract 353 51.6 CO2 + methanol McDaniel et al., 2001

Table A-3. Wood and fiber material extractions carried out in SCF

A-2. Soil remediation

Decontamination of soils using SCF is an attractive process compared to extraction with liquid solvents because no toxic residue is left in the remediated soil and in contrast to thermal desorption soils are not burned. Specially, the removal of typical industrial wastes (PAH, PCB, fuels) can be easily achieved through SCE (see T able A-4).

Removal T P Contaminant Solvent References efficiency [K] [M Pa] PCB 1,1,1-trichloroethane (DDT) 70 – 90 % 313 10 CO2 Brady et al., 1987 Toxaphene

CO2 CO2 + methanol Polycyclic aromatic Acetone Barnabas et al., 1995 ~ 100 % 313 – 423 40 hydrocarbons (PAH) Hexane Lojková et al., 2005 Methanol Toluene

CO2 Carbamate pesticides 39.6 – 91.7 % 327 37.8 Izquierdo et al., 1996 CO2 + methanol Gasoline > 88 % 353 34 CO2 Yang et al., 1995 Diesel

Table A-4. Examples of applications of SCE to soil remediation

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A-3. Heterogeneous catalysis

The use of SCF as a reaction media can be a real advantage when using heterogeneous catalysts, since the diffusion rates are enhanced compared to reactions in the liquid phase. See Table A-5 for further details on chemical reactions carried out in SCF.

Catalyst T P Reaction Solvent References type [K] [MPa] ALKYLATION Isopentane and isobutene Isopentane Zeolite 323 – 473 3.5 – 5 Fan et al., 1997a Isobutane and isobutene Isobutane Clark and Subramaniam, 1-butene and isobutane Zeolite CO2 323 – 413 3.4 – 15.5 1998 Mesytilene and propene Propene Deloxan 433 – 573 15 – 20 Hitzler et al., 1998 Mesytilene and propan-2-ol CO2 CRACKING Heptane Zeolite Heptane 598 3.4 Dardas et al., 1996 DISPROPORTIONATION Toluene to p-xylene and Zeolite 593 – 598 3.36 – 5.6 Collins et al., 1998 benzene 1,4-diisopropylbenzene to Tiltscher et al., 1984 Benzene cumene and Zeolite 533 20 Tiltscher and Hofmann, n-pentane 1,3,5-triisopropylbenzene 1986 Ethylbenzene to benzene Butane Niu and Hoffmann, Zeolite 573 – 673 5 and diethylbenzene Pentane 1995, 1997 ESTERIFICATION

Oleic acid and methanol Sulfonic CO2 > 823 0.95 – 1.3 Vieville et al., 1993 FISHER – TROPSCH SYNTHESIS

Ru/Al2O3 Yokota et al., n-hexane 313 – 341 4.5 CO and H2 to liquid Co/SiO2 1990, 1991a, 1991b hydrocarbons Snavely and Fe n-hexane 513 7 – 8 Subramaniam, 1997 ISOMERIZATION

CO2+n-pentane Clark and Subramaniam, 1-hexene Pt/Al2O3 291 18 CO2+n-hexane 1996 1-hexene to olefinic McCoy and Subramaniam, Pt/Al2O3 300.7 27.7 oligomers 1995 OXIDATION

Toluene to benzaldehyde Co/Al2O3 CO2 281 8 Dooley and Knopf, 1987 Isobutane to tert-butyl Pd/C 426 4.4 – 5.4 Fan et al., 1997b alcohol HYDROGENATION

Fats and oils Deloxan CO2 - Propane 323 – 433 8 – 16 Tacke et al., 2003 Acetophenone Deloxan CO2 - Propane 313 – 593 6 – 12 Hitzler and Poliakoff, 1997 Cyclohexene

Cyclohexene Pd/C CO2 343 13.6 Arunajatesan et al., 2001 Unsaturated ketones Pd/Al2O3 CO2 323 – 493 12 – 17.5 Bertucco et al., 1997 van den Hark et al., Fatty acid methyl esters Cu Propane 473 – 573 15 1999, 2000, 2001 α - pirene Pd/C CO2 323 14 Chouchi et al., 2001 Macher and Holmqvist, Palm oil Pd/C Propane 338 - 408 15 2001 Soybean oil Ni CO2 393 - 413 14 King et al., 2001 Propane Ramirez et al., 2004 Sunflower oil Pd/C 428 - 488 20 Dimethyleter Ramirez, 2005

Table A-5. Survey of heterogeneous catalytic reactions carried out under supercritical conditions (Baiker, 1999; Ramirez et al., 2002)

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A-4. CFD in chemical engineering and process equipment design

Typical process equipment, key design issues, and perspectives on the current status of CFD for simulating flows of practical interest in chemical engineering are summarized in Tables A-6 to A-11. (Joshi and Ranade, 2003).

Typical Pipes, pipe fittings, T mixers, valves, flow meters, membrane modules, heat exchangers, equipment monoliths, etc. Design Pressure drop, heat transfer coefficient, mixing, residence time distribution, issues / identification of hot spots/dead regions, influence of non-uniform material properties, applications extrapolation to non-standard configurations, end effects. CURRENT STATUS Excellent for laminar flow of simple fluids. Knowledge Constitutive equations for complex fluids are not well developed. of physics Reasonable for turbulent flow of simple fluids. Turbulence models for non-Newtonian fluids are not well developed. Commercial CFD codes/models allow implementation of different constitutive equations/non-uniform material properties. Grid quality, constitutive equations and turbulence models control the quality of simulations. CFD codes / It is essential to ensure that the appropriate turbulence model is selected for simulating design complex turbulent flows even for qualitative screening of configurations; reasonable applicability guidelines for such a selection are available. Applicability is generally excellent for laminar flows of Newtonian and simple non- Newtonian fluids; limited for viscoelastic/structured fluids. CFD simulations are widely used for addressing key design issues. PATH FORWARD Geometrical complexity: grid quality, curvature effects. Limitation of Rheological complexity: constitutive equations, solvers/algorithms. current models Turbulence: transition regime, transport rates near walls, computing resources. Reactive mixing: computing resources, closure models. Experiments Physical models Influence of curvature and severe changes Influence of small-scale features on in flow direction on turbulence or complex performance (Liou et al., 2002). fluids (Suga, 2003). Behavior of complex fluids in non- Constitutive equations based on molecular Development rheometric flows (Rothstein and structure (van Ruymbeke et al., 2002). needed for McKinley, 2001). overcoming Data at the onset of turbulence (Palikaras Turbulence models adaptable to the the et al., 2002). resolution requirements (Speziale, 1998). limitations Understanding the role of unsteady flows Measurement of wall transport rates for (spatially or temporally) in mixing and model discrimination (Vogel, 1984). other transport processes (Jana et al., 1994). Measurement of local concentration and segregation profiles (Verschuren et al., 2002).

Table A-6. Typical process equipment, key design issues, and perspectives on the current status of CFD for simulating single-phase flows through conduits/channels (Joshi and Ranade, 2003)

- 154 - Appendix A

Typical Falling film evaporators, fiber spinning, spray coating, multiphase flow equipment through monoliths, etc. Shape of free surface/film thickness, influence of surface characteristics Design (roughness/wetting), non-uniform material properties, heat/mass transfer issues / coefficients, hot spots, learning tool for developing macroscopic closure applications models. CURRENT STATUS Basic models to capture the role of surface forces on the shape of the interface between immiscible phases are available. Knowledge Understanding of surface characteristics, contact angle, and wetting and wall of physics adhesion is not adequate. Quantitative prediction of instabilities and regime transitions is not yet adequately possible. Commercial CFD codes allow simulating free surface flows with the “volume of fluid” (VOF) approach or other front tracking methods. Demands on grid resolution/computing resources are high: usually three- dimensional simulations are needed; small time step and several iterations per CFD codes / time step are needed for simulating transients accurately; convergence design behavior is sensitive to the grid quality applicability VOF-based codes are relatively robust compared to those with surface tracking; simulations require expert intervention. CFD simulations are used more as a learning tool than as a design tool; in some instances, calibrated models may be used for design. PATH FORWARD Wetting and wall adhesion: hysteresis in contact angle, grid quality, curvature effects. Limitation of Transport at interface: capturing small-scale features of the interface, current computation of gradients at the interface. models Flow regimes: grid quality, transport of the interface, computational resources. Prediction of “die-swell” and other peculiar characteristics of viscoleastic fluids. Experiments Physical models Characterization of wetting and drop Models for wetting characteristics dynamics on different surfaces (Ted (Fukai et al., 1995) Mao et al., 1997). Influence of gradients of surface Data on mass and heat transfer through Development tension on transport rates the interface needed for (Gulawani, 2002). overcoming Characterization of film flows and Capturing small-scale structures on the associated instabilities (Ambrosini et al., the interface. limitations 2002). Data on the influence of viscoelasticity Interface models to capture regime on evolution of free surfaces (Cooper- transitions. White et al., 2002). Constitutive equations suitable for

practical flows of interest.

Table A-7. Typical process equipment, key design issues, and perspectives on the current status of CFD for simulating free surface flows (Joshi and Ranade, 2003)

- 155 - Appendix A

Typical Rotating disk contactors, stirred reactors/mixers, pumps/turbines, screw extruders, equipment rotary kilns/dryers, ball mills, centrifuges, etc. Design Number, shape, size, location and speed of impellers or rotating walls, issues / mixing/residence time distribution, heat and mass transfer, scale-up/scale-down applications CURRENT STATUS Adequate for single-phase flows in stirred vessels. Knowledge Models for simulating dispersion or suspension of multiphase flows are inadequate. of physics Models for granular flows in kilns/ball mills are inadequate. Commercial CFD codes/models allow simulations of rotating elements with either quasi-steady or sliding mesh approaches. Fine resolution is needed to capture vortices around rotating elements; full transient simulations based on the sliding mesh approach are very computation intensive. CFD codes / Prediction of mean flow is reasonably good. design Quantitative prediction of turbulence characteristics is still not good. applicability A priori prediction of multiphase flows in stirred vessels or rotary kiln is not possible; regime-specific models with calibrated parameters may provide useful design information. CFD simulations are used for understanding different design configurations mainly to assist engineering decision making. PATH FORWARD Free surface at the top/vortex/gas entrainment. Limitation of Turbulence: transition regime, non-Newtonian fluids. current Low-frequency unsteady flows. models Multiphase flows: coalescence/agglomeration, break-up, accumulation behind rotating elements, heat and mass transfer. Experiments Physical models Data near the top free surface (Desai and Calibration of parameters of the turbulence Joshi, 1996). models. Measurement of turbulence kinetic Influence of the free surface/vortex on flow energy, stresses and energy dissipation in the stirred vessels (Ciofalo et al., 1996) rate (Joshi et al., 2001) Characterization of unsteady flow and its Turbulence models/wall functions for non- impact on the performance (Kresta and Newtonian fluids (Son and Singh, 2002). Wood, 1993). Development Characterization of cavities formed Ways of capturing low-frequency flow needed for behind rotating blades (Lu and Ju, 1989). structures. Local measurements of phase fractions, overcoming Modified wall functions to capture heat phase velocities, and particle sizes at the transfer near walls with complex higher phase fractions (Bombač et al., limitations curvatures (Churchill and Zajic, 2002). 1997). Local measurements of heat transfer Interface closures and coalescence/break- coefficients at different coil/jacket up models for high phase factions. configurations (Karcz, 1999). Measurements of angle of internal Improved models for estimating the friction, coefficient of restitution, granular frictional viscosity of solid particles sliding pressure, and temperature for solid- on each other. liquid flows. Models for simulating formation of gas

cavities behind the blades

Table A-8. Typical process equipment, key design issues, and perspectives on the current status of CFD for simulating stirred or rotating vessels (Joshi and Ranade, 2003)

- 156 - Appendix A

Bubble/slurry columns fluidized beds, spray columns/extraction columns, cyclones, Typical settling tanks, crystallizers/precipitators, distillation trays, condensers/evaporators, equipment furnace/boilers, mist eliminators. Design Distributor design, flow regime, minimal fluidization/suspension velocity, sensitivity issues / with hardware changes, distribution of dispersed phase and interfacial area, mass and applications heat transfer, entrainment, scale-up, erosion. CURRENT STATUS Reasonable for dilute multiphase flows. Knowledge Inadequate for dense multiphase flows; closures for interface coupling for turbulent, of physics dense dispersions are not adequate. Models involve several parameters, which have to be obtained from experimental data. Eulerian-Lagrangian and Eulerian-Eulerian approaches are used for simulating diluted and dense flows respectively; several particle trajectories need to be simulated for an adequate picture. A priori prediction of flow regimes is still not possible. CFD codes / Models are often calibrated without obtaining grid-independent results; ad hoc models design to simulate the influence of turbulence or other particles are used. applicability Though basic solvers for Eulerian-Lagrangian and Eulerian-Eulerian approaches are available in commercial CFD codes, user-defined functions are needed to implement problem-specific sub-models and closures. Use in practical design is often indirect: for qualitative screening and for assisting engineering decision making rather than for quantitative simulations. PATH FORWARD Trajectory simulations in turbulent flows: drag coefficient, lift coefficient and virtual mass coefficient, turbulent dispersion of particles. Limitation of Turbulence caused by dispersed particles. current Momentum transport between different dispersed phases. models Agglomeration/coalescence/break-up process. Phase change (evaporation/condensation). Experiments Physical models Direct numerical simulation (lattice Unambiguous experimental data to Boltzmann or surface tracking) of determine drag, lift and virtual mass multiphase flows to provide theoretical coefficients under different conditions basis to empirical correlations of drag, lift (Tomiyama et al., 2002). and virtual mass transfer coefficients (Sankaranarayanan and Sundaresan, 2002). Experiments allowing separation of Models to ensure correct Lagrangian dispersion effects from lift and other correlations (Gouesbet and Berlemont, Development effects. 1999). needed for Data on Lagrangian quantities Improved models for the interface overcoming (Cassanello et al., 2001). momentum, energy and mass transfer. the Experiments to quantify the fraction of limitations Better understanding of the formation of energy dissipated in the bulk of the agglomeration/clusters and their influence continuous phase (away from the of transport rates (Harris et al., 2002). interface). Local measurements of size distribution Models to estimate the effective dispersion to track evolution of it within the vessel coefficients as a function of particle size and (Buwa and Ranade, 2002). local turbulence length scales. Measurement of the interfacial area and Guidelines to select appropriate local mass transfer coefficients (Sun et al., coalescence/break-up kernels. 2003).

Table A-9. Typical process equipment, key design issues, and perspectives on the current status of CFD for simulating multiphase flows (Joshi and Ranade, 2003)

- 157 - Appendix A

Typical Reactors for fast reactions (comparable time scales of mixing and reactions), combustors, equipment burners, etc. Design Selectivity and yield, complete combustion and energy efficiency, NO formation, issues / x nozzle designs, etc. applications CURRENT STATUS Reasonable for single phase laminar flow. Knowledge Closures for turbulent reactive mixing are available only for simple reaction schemes. of physics Inadequate models for interfacial area limit modeling of multiphase reactive flows. Single phase reactive mixing models may be solved using the existing framework with additional closure models (probability density function/phenomenological approaches); user defined functions are needed for implementing specific phenomenological closures. CFD codes / Particle level reactions may be simulated with the Eulerian-Lagrangian approach. design Simulations of reactive flows are computationally very demanding, and it is difficult to applicability eliminate the influence of numerical issues completely. CFD simulations are used to address design issues of equipment involving single phase or dilute multiphase reactive flows. Capability of simulating multiphase reactive systems with the Eulerian-Eulerian or VOF approach is still in the primitive stages. PATH FORWARD Laminar reactive mixing in complex fluids. Limitation of current Turbulent reactive mixing in liquids. models Dilute/dense multiphase reactive flows. Experiments Physical models Better understanding of interaction between Measurements of reduction in striation turbulent, mixing and reaction for cases with thickness with time under well- one of the reactants or products existing in a characterized flows for complex fluids different immiscible phase (Marchisio et al., (Fourcade et al., 2001). 2001). Experiments to quantitatively measure Quantification of the role of complex the reduction in scale of mixing and Development rheology on stretching and folding to create intensity of mixing (Buchmann and needed for large interfacial areas. Mewes, 2000). overcoming Measurements of cross correlations of Closure models for simulating complex the concentrations for improved closures. reaction schemes. limitations Measurements of local interfacial area Models/correlations for interface transport and transport coefficients from rates from drops/particles with phase agglomerated particle clusters (van der change. Bosch, 1998). Measurements of transport coefficients Models of cluster formation and to predict under evaporation or condensation. their influence on reaction rates. Single particle combustion data under

controlled flow situations.

Table A-10. Typical process equipment, key design issues, and perspectives on the current status of CFD for simulating reactive flows (Joshi and Ranade, 2003)

- 158 - Appendix A

Typical Monoliths, fixed beds, packed columns, trickle-bed reactors, filters, etc. equipment Design Pressure drop, flow maldistribution and channelling, mixing and residence time issues / distribution, heat/mass transfer coefficients, scale-up. applications CURRENT STATUS

Lumped models with or without isotropic porosity are used. Knowledge of physics Reasonable results for single phase flows; closure models suitable for multiphase flows through packed beds are not adequate. Commercial CFD codes/models allow simulations of single phase flows through porous media with isotropic or non-isotropic permeability and inertial resistance coefficients. CFD codes / Detailed modeling of a packed bed, which is very computational intensive, is being design explored in recent studies. applicability Capability of simulating multiphase flow through packed beds is almost nonexistent except via user defined functions based on empirically calibrated closure models. Used for designing equipment with single phase flow through a porous medium; use for designing multiphase flow through a packed bed is in the primitive stage. PATH FORWARD Representing void space in packed beds: grid quality, computing resources. Limitation of Closures for multiphase flow through packed beds: interfacial area, drag. current models Simulation of flow regimes/transition and unsteady flows. Mixing and liquid dispersion: role of capillary forces/wetting. Experiments Physical models Measurements of porosity distribution (with different length scales) within a Direct numerical simulations or VOF packed bed (mean, axially averaged, simulations are needed to guide the standard deviation and so on) with development of closures. quantification of the way of packing (Sharma et al., 2001). Pressure drop measurements along with Models to simulate different packing local turbulence characteristics (Niven, possibilities (Spedding and Spencer, 1995). Development 2002). needed for Local phase distributions and velocity Interface closure models for estimating gas- overcoming measurements for gas-liquid flow solid, gas-liquid and liquid-solid phases the through well-characterized packed beds under different flow regimes (Jiang et al., limitations (Gladden et al., 2002). 2002). Detailed measurements of unsteady flow Models to represent transient or low Re regimes (quantification of key spatial and turbulent flow through complex geometry temporal scales). with severe curvature. Experimental data (global as well as local Quantification of the role of gradients of measurements) on liquid dispersion, porosity and capillary forces on liquid bypass and channelling (Tsochatzidis et dispersion (Yin et al., 2000). al., 2002). Quantitative representation of the role of

wetting and hysteresis on contact angle.

Table A-11. Typical process equipment, key design issues, and perspectives on the current status of CFD for simulating flow through packed beds (Joshi and Ranade, 2003)

- 159 - Appendix A

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- 167 -

APPENDIX B

TURBULENCE MODELS Transport equations, turbulent quantities and convective heat and mass transfer modeling

Michael Graf, « Turbulence »

Appendix B

B-1. The Spalart-Allmaras model

B-1.1. Transport equation

In turbulence models that employ the Boussinesq approach, the central issue is how the eddy viscosity is computed. The model proposed by Spalart and Allmaras (1992) solves a transport equation for a quantity that is a modified form of the turbulent kinematic viscosity. The ~ transported variable in the Spalart-Allmaras model, ν , is identical to the turbulent kinematic viscosity except in the near-wall (viscous-affected) region. The transport equation for ν~ is

2 ⎡ ⎧ ~ ⎫ ⎛ ~ ⎞ ⎤ ∂ ~ ∂ ~ 1 ⎢ ∂ ⎪ ~ ∂ν ⎪ ⎜ ∂ν ⎟ ⎥ ()ρ ν + ()ρ ν ui = Gν + ⎨()µ + ρ ν ⎬ + Cb2 ρ −Yν + Sν~ ∂t ∂x σ ~ ⎢∂x ⎪ ∂x ⎪ ⎜ ∂x ⎟ ⎥ i ν ⎣ j ⎩ j ⎭ ⎝ j ⎠ ⎦ [B-1]

where Gν is the production of turbulent viscosity and Yν is the destruction of turbulent viscosity that occurs in the near-wall region due to wall blocking and viscous damping. σν~ and

Cb2 are constants and ν is the molecular kinematic viscosity. Sν~ is a user-defined source term. Note that since the turbulence kinetic energy is not calculated in the Spalart-Allmaras model, the last term in Eq. [2.1-8] is ignored when estimating the Reynolds stresses.

B-1.2. Modeling the turbulent viscosity

The turbulent viscosity, µt , is computed from

~ µt = ρνf v1 [B-2]

where the viscous damping function, f v1 , is given by

Χ 3 f v1 = 3 3 [B-3] Χ + Cv1

ν~ Χ ≡ [B-4] ν

B-1.3. Modeling the turbulent production

The production term, Gν , is modeled as

~ ~ Gν = Cb1ρ Sν [B-5]

~ ν~ Θ ≡ Θ + f [B-6] κ 2 d 2 v2

- 171 - Appendix B

Χ f v2 = 1− [B-7] 1+ Χ f v1

Cb1 and κ are constants, d is the distance from the wall, and Θ is a scalar measure of the deformation tensor. By default, Θ is based on the magnitude of the vorticity:

Θ ≡ 2Ωij Ωij [B-8]

where Ωij is the mean rate-of-rotation tensor and is defined by

1 ⎛ ∂u ∂u ⎞ Ω = ⎜ i − j ⎟ ij ⎜ ⎟ [B-9] 2 ⎝ ∂x j ∂xi ⎠

B-1.4. Modeling the turbulent destruction

The destruction term is modeled as

2 ⎛ν~ ⎞ Yν = Cw1ρ f w ⎜ ⎟ [B-10] ⎝ d ⎠

1 6 6 ⎡ 1+ Cw3 ⎤ f w = g⎢ 6 6 ⎥ [B-11] ⎣ g + Cw3 ⎦

6 g = r + Cw2 (r − r) [B-12]

ν~ r ≡ ~ [B-13] Θκ 2d 2

~ Cw1 , Cw2 and Cw3 are constants, and Θ is given by Eq. [B-6].

B-1.5. Model constants

The model constants have the following default values (Spalart and Allmaras, 1992):

2 C = 0.1355; C = 0.622; σ ~ = ; C = 7.1; b1 b2 ν 3 v1 [B-14] Cb1 1+ Cv2 Cw1 = 2 + ; Cw2 = 0.3; Cw3 = 2.0; κ = 0.4187; κ σν~

- 172 - Appendix B

B-1.6. Wall boundary conditions

~ At walls, the modified turbulent kinematic viscosity, ν , is set to zero.

When the mesh is fine enough to resolve the laminar sublayer, the wall shear stress is obtained from the laminar stress-strain relationship:

u ρ u d = t [B-15] ut µ

If the mesh is too coarse to resolve the laminar sublayer, it is assumed that the centroid of the wall-adjacent cell falls within the logarithmic region of the boundary layer, and the law-of-the- wall is employed:

u 1 ⎛ ρ ut d ⎞ = ln9.793⎜ ⎟ [B-16] ut 0.4187 ⎝ µ ⎠

where u is the velocity parallel to the wall and ut is the shear velocity.

B-1.7. Convective heat and mass transfer modeling

Turbulent heat transport is modeled using the concept of Reynolds' analogy to turbulent momentum transfer. The "modeled'' energy equation is thus given by the following:

∂ ∂ ∂ ⎡⎛ C p µt ⎞ ∂T ⎤ ()ρE + []u ()ρE + p = ⎜k + ⎟ + u τ + S [B-17] i ⎢⎜ ⎟ i ()ij eff ⎥ h ∂t ∂xi ∂x j ⎣⎢⎝ Prt ⎠ ∂x j ⎦⎥ where k is the thermal conductivity, E is the total energy, and τ is the deviatoric stress ( ij )eff tensor, defined as

⎛ ∂u j ∂u ⎞ 2 ∂u ()τ = µ ⎜ + i ⎟ − µ i δ [B-18] ij eff eff ⎜ ⎟ eff ij ⎝ ∂xi ∂x j ⎠ 3 ∂xi

τ The term involving ( ij )eff represents the viscous heating. The default value of the turbulent Prandtl number is 0.85. Turbulent mass transfer is treated similarly, with a default turbulent Schmidt number of 0.7. Wall boundary conditions for scalar transport are handled analogously to momentum, using the appropriate "law-of-the-wall''.

B-2. The κ − ε family models

This section presents the standard, RNG, and realizable κ − ε models. All three models have similar forms, with transport equations for κ and ε . The major differences in the models are as follows:

- 173 - Appendix B

• the method of calculating turbulent viscosity

• the turbulent Prandtl numbers governing the turbulent diffusion of κ and ε .

• the generation and destruction terms in the equation.

The transport equations, methods of calculating turbulent viscosity, and model constants are presented separately for each model. The features that are essentially common to all models follow, including turbulent production, generation due to buoyancy, accounting for the effects of compressibility, and modeling heat and mass transfer.

B-2.1. The standard κ − ε model

The standard κ − ε model (Launder and Spalding, 1972) is a semi-empirical model based on model transport equations for the turbulence kinetic energy (κ ) and its dissipation rate ( ε ). The model transport equation for κ is derived from the exact equation, while the model transport equation for ε was obtained using physical reasoning and bears little resemblance to its mathematically exact counterpart.

In the derivation of the κ − ε model, it was assumed that the flow is fully turbulent, and the effects of molecular viscosity are negligible. The standard κ − ε model is therefore valid only for fully turbulent flows.

B-2.1.1. Transport equations

The turbulence kinetic energy, κ , and its rate of dissipation, ε , are obtained from the following transport equations:

∂ ∂ ∂ ⎡⎛ µ ⎞ ∂κ ⎤ ()ρ κ + ()ρ κ u = ⎢⎜ µ + t ⎟ ⎥ + G − ρε + S [B-19] ∂t ∂x i ∂x ⎜ Pr ⎟ ∂x κ κ i j ⎣⎢⎝ ()t κ ⎠ j ⎦⎥

∂ ∂ ∂ ⎡⎛ µ ⎞ ∂ε ⎤ ε ε 2 ()ρε + ()ρε u = ⎢⎜ µ + t ⎟ ⎥ + C ()G + C G − C ρ + S ∂t ∂x i ∂x ⎜ Pr ⎟ ∂x 1ε κ k 3ε b 2ε κ ε i j ⎣⎢⎝ ()t ε ⎠ j ⎦⎥ [B-20]

In these equations, Gκ represents the generation of turbulence kinetic energy due to the mean velocity gradients, Gb is the generation of turbulence kinetic energy due to buoyancy, Ym represents the contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate, C1ε , C2ε , and C3ε are constants, and Sκ and Sε are user-defined source terms.

B-2.1.2. Modeling the turbulent viscosity

The turbulent (or eddy) viscosity , µ t , is computed by combining κ and ε as follows:

- 174 - Appendix B

κ 2 µ = ρ C [B-21] t µ ε

where C µ is a constant.

B-2.1.3. Model constants

The model constants have the following default values (Launder and Spalding, 1972):

C1ε = 1.44; C2ε = 1.92; Cµ = 0.09; (Prt )κ = 1.0; (Prt )ε = 1.3; [B-22]

These default values have been determined from experiments with air and water for fundamental turbulent shear flows including homogeneous shear flows and decaying isotropic grid turbulence. They have been found to work fairly well for a wide range of wall-bounded and free shear flows.

B-2.2. The RNG κ − ε model

The RNG-based κ − ε turbulence model (Choudhury et al., 1993) is derived from the instantaneous Navier-Stokes equations, using a mathematical technique called “renormalization group” (RNG) methods. The analytical derivation results in a model with constants different from those in the standard κ − ε model, and additional terms and functions in the transport equations for κ and ε .

B-2.2.1. Transport equations

The RNG κ − ε model has a similar form to the standard κ − ε model:

∂ ∂ ∂ ⎛ ∂κ ⎞ ρκ + ρκ u = ⎜α µ ⎟ + G + G − ρε − Y + S () ()i ⎜ κ eff ⎟ κ b M κ [B-23] ∂t ∂xi ∂x j ⎝ ∂x j ⎠

∂ ∂ ∂ ⎛ ∂ε ⎞ ε ε 2 ρε + ρε u = ⎜α µ ⎟ + C G + C G − C ρ − R + S () ()i ⎜ ε eff ⎟ 1ε ()κ 3ε b 2ε ε ε ∂t ∂xi ∂x j ⎝ ∂x j ⎠ κ κ [B-24]

In these equations, Gκ represents the generation of turbulence kinetic energy due to the mean velocity gradients, Gb is the generation of turbulence kinetic energy due to buoyancy, and Ym represents the contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate. The quantities α κ and α ε are the inverse effective Prandtl numbers for

κ and ε , respectively. Sκ and Sε are user-defined source terms.

- 175 - Appendix B

B-2.2.2. Modeling the effective viscosity

The scale elimination procedure in RNG theory results in a differential equation for turbulent viscosity:

⎛ ρ 2κ ⎞ νˆ d⎜ ⎟ = 1.72 dνˆ [B-25] ⎜ ⎟ 3 ⎝ εµ ⎠ νˆ −1+ Cv

µeff νˆ = [B-26] µ

Cν ≈ 100 [B-27]

Eq. [B-25] is integrated to obtain an accurate description of how the effective turbulent transport varies with the effective Reynolds number (or eddy scale), allowing the model to better handle low-Reynolds-number and near-wall flows .

In the high-Reynolds-number limit, Equation [B-25] gives

κ 2 µ = ρ C [B-28] t µ ε

with C µ = 0.0845, derived using RNG theory. It is interesting to note that this value of C µ is very close to the empirically-determined value of 0.09 used in the standard κ − ε model.

B-2.2.3. Calculating the inverse effective Prandtl numbers

The inverse effective Prandtl numbers, α κ and α ε , are computed using the following formula derived analytically by the RNG theory:

0.6321 0.3679 α −1.3929 α + 2.3929 µ = mol [B-29] α 0 −1.3929 α 0 + 2.3929 µeff

where α 0 = 1.0. In the high-Reynolds-number limit ( µ mol / µ eff << 1), α κ = α ε ≈ 1.393

B-2.2.4. The Rε term in the ε equation

The main difference between the RNG and standard κ − ε models lies in the additional term in the ε equation given by

3 ⎛ η ⎞ Cµ ρη ⎜1− ⎟ 2 ⎝ η0 ⎠ ε R = [B-30] ε 1+ βη 3 κ

- 176 - Appendix B

η ≡ Vκ / ε , η 0 = 4.38, β = 0.012. [B-31]

The effects of this term in the RNG ε equation can be seen more clearly by rearranging Eq. [B- 24]. Using Eq. [B-30], the third and fourth terms on the right-hand side of Eq. [B-24] can be merged, and the resulting ε equation can be rewritten as

∂ ∂ ∂ ⎛ ∂ε ⎞ ε ε 2 ρε + ρε u = ⎜α µ ⎟ + C G + C G − C * ρ () ()i ⎜ ε eff ⎟ 1ε ()κ 3ε b 2ε [B-32] ∂t ∂xi ∂x j ⎝ ∂x j ⎠ κ κ

3 ⎛ η ⎞ Cµη ⎜1− ⎟ * ⎝ η0 ⎠ C ≡ C + [B-33] 2ε 2ε 1+ βη 3

* In regions where η <η 0 , the Rε term makes a positive contribution, and C2ε becomes larger * than C2ε . In the logarithmic layer, for instance, it can be shown that η ≈ 3.0, giving C2ε ≈ 2.0, which is close in magnitude to the value of C2ε in the standard κ −ε model (1.92). As a result, for weakly to moderately strained flows, the RNG model tends to give results largely comparable to the standard κ −ε model.

In regions of large strain rate (η >η 0 ), however, the Rε term makes a negative contribution, * making the value of C2ε less than C2ε . In comparison with the standard κ −ε model, the smaller destruction of ε augments ε , reducing κ and, eventually, the effective viscosity. As a result, in rapidly strained flows, the RNG model yields a lower turbulent viscosity than the standard κ −ε model.

Thus, the RNG model is more responsive to the effects of rapid strain and streamline curvature than the standard κ −ε model, which explains the superior performance of the RNG model for certain classes of flows.

B-2.2.5. Model constants

The model constants have values derived analytically by the RNG theory. These values are:

C1ε = 1.42; C2ε = 1.68; [B-34]

B-2.3. The realizable κ − ε model

In addition to the standard and RNG-based κ −ε models, Shih et al. (1995) proposed the so- called realizable κ −ε model. The term “realizable” means that the model satisfies certain mathematical constraints on the normal stresses, consistent with the physics of turbulent flows. To understand this, consider combining the Boussinesq relationship (Eq. [2.1-8]) and the eddy viscosity definition (Eq. [B-21]) to obtain the following expression for the normal Reynolds stress in an incompressible strained mean flow:

2 2 ∂U u = κ − 2ν [B-35] 3 t ∂x

- 177 - Appendix B

2 Using Eq. [B-21] for ν t ≡ µt / ρ , one obtains the result that the normal stress, u , which by definition is a positive quantity, becomes negative, i.e., “non-realizable”, when the strain is large enough to satisfy

κ ∂U 1 ≥ ≈ 3.7 [B-36] ε ∂x 3Cµ

2 2 2 Similarly, it can also be shown that the Schwarz inequality for shear stresses ( uα u β ≤ uα u β ; no summation over α and β ) can be violated when the mean strain rate is large. The most straightforward way to ensure the realizability (positivity of normal stresses and Schwarz inequality for shear stresses) is to make C µ variable by sensitizing it to the mean flow (mean deformation) and the turbulence (κ , ε ). The notion of variable C µ is suggested by many modelers including Reynolds (1987), and is well substantiated by experimental evidence. For example, C µ is found to be around 0.09 in the inertial sublayer of equilibrium boundary layers, and 0.05 in a strong homogeneous shear flow.

Another weakness of the standard κ −ε model or other traditional κ −ε models lies with the modeled equation for the dissipation rate. The well-known round-jet anomaly (named based on the finding that the spreading rate in planar jets is predicted reasonably well, but prediction of the spreading rate for axisymmetric jets is unexpectedly poor) is considered to be mainly due to the modeled dissipation equation.

The realizable κ −ε model was intended to address these deficiencies of traditional κ −ε models by adopting the following:

• a new eddy-viscosity formula involving a variable originally proposed by Reynolds (1987).

• a new model equation for dissipation based on the dynamic equation of the mean- square vorticity fluctuation.

B-2.3.1. Transport equations

The modeled transport equations for κ and ε in the realizable κ −ε model are

∂ ∂ ∂ ⎡⎛ µ ⎞ ∂κ ⎤ ()ρκ + ()ρκ u = ⎢⎜ µ + t ⎟ ⎥ + G + G − ρε − Y + S [B-37] ∂t ∂x j ∂x ⎜ Pr ⎟ ∂x κ b M κ j j ⎣⎢⎝ ()t k ⎠ j ⎦⎥

∂ ∂ ∂ ⎡⎛ µ ⎞ ∂ε ⎤ ε 2 ε ()ρε + ()ρε u = ⎢⎜ µ + t ⎟ ⎥ + ρC Vε − ρC + C C G + S ∂t ∂x j ∂x ⎜ Pr ⎟ ∂x 1 2 1ε κ 3ε b ε j j ⎣⎢⎝ ()t ε ⎠ j ⎦⎥ κ + νε [B-38] ⎡ η ⎤ κ C1 = max⎢0.43, ⎥; η = V ; V = 2VijVij [B-39] ⎣ η + 5⎦ ε

- 178 - Appendix B

In these equations, Gκ represents the generation of turbulence kinetic energy due to the mean velocity gradients, Gb is the generation of turbulence kinetic energy due to buoyancy, and Ym represents the contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate. Sκ and Sε are user-defined source terms.

Note that the κ equation (Eq. [B-37]) is the same as that in the standard κ −ε model (Eq. [B- 19]) and the RNG κ −ε model (Eq. [B-23]), except for the model constants. However, the form of the ε equation is quite different from those in the standard and RNG-based κ −ε models (Eqs. [B-20] and [B-24]). One of the noteworthy features is that the production term in the equation (the second term on the right-hand side of Eq. [B-38]) does not involve the production of κ ; i.e., it does not contain the same Gκ term as the other κ −ε models. It is believed that the present form better represents the spectral energy transfer. Another desirable feature is that the destruction term (the next to last term on the right-hand side of Eq. [B-38]) does not have any singularity; i.e., its denominator never vanishes, even if κ vanishes or becomes smaller than zero. This feature is contrasted with traditional κ −ε models, which have a singularity due to κ in the denominator.

B-2.3.2. Modeling the turbulent viscosity

As in other κ −ε models, the turbulent viscosity is computed from:

κ 2 µ = ρC [B-40] t µ ε

The difference between the realizable κ −ε model and the standard and RNG κ −ε models is that C µ is no longer constant. It is computed from

1 C = [B-41] µ κU * A + A o s ε

* ~ ~ U ≡ VijVij + Ωij Ωij [B-42]

~ Ωij = Ωij − 2ε ijkωk [B-43]

Ωij = Ωij − ε ijkωk [B-44]

where Ω ij is the mean rate-of-rotation tensor viewed in a rotating reference frame with the angular velocity ω k . The model constants A0 and As are given by

Ao = 4.04; As = 6 cosφ [B-45]

1 −1 VijV jkVki ~ 1 ⎛ ∂u j ∂u ⎞ φ = cos 6W ; W = ; V = V V ; V = ⎜ + i ⎟ [B-46] () ~ 3 ij ij ij ⎜ ⎟ 3 V 2 ⎝ ∂xi ∂x j ⎠

- 179 - Appendix B

It can be seen that C µ is a function of the mean strain and rotation rates, the angular velocity of the system rotation, and the turbulence fields.

B-2.3.3. Model constants

The model constants have been established to ensure that the model performs well for certain canonical flows. The model constants are:

C1ε = 1.44; C2 = 1.9; ()Prt κ = 1.0; (Prt )ε = 1.2; [B-47]

B-2.4. Modeling the turbulent production in the κ − ε models

The term Gκ , representing the production of turbulence kinetic energy, is modeled identically for the standard, RNG, and realizable κ −ε models. From the exact equation for the transport of , this term may be defined as

∂u j Gκ = −ρui′u′j [B-48] ∂xi

To evaluate Gκ in a manner consistent with the Boussinesq hypothesis,

2 Gκ = µtV [B-49] where V is the modulus of the mean rate-of-strain tensor, defined by Eqs. [B-39] and [B-46].

B-2.5. Standard wall functions

Wall functions are a collection of semi-empirical formulas and functions that in effect "bridge'' or "link'' the solution variables at the near-wall cells and the corresponding quantities on the wall. The wall functions comprise:

• laws-of-the-wall for mean velocity and temperature (or other scalars)

• formulas for near-wall turbulent quantities

The standard wall functions are based on the proposal of Launder and Spalding (1974), and have been most widely used for industrial flows.

B-2.5.1. Momentum

The law-of-the-wall for mean velocity yields

* 1 * U = ln()9.793y [B-50] 0.4187

- 180 - Appendix B

1 1 4 2 * U PCµ κ P U ≡ [B-51] τ w / ρ

1 1 4 2 * ρCµ κ P yP y ≡ [B-52] µ

The logarithmic law for mean velocity is known to be valid for 30 < y * < 300. In the FV code, the log-law is employed when y * > 11.225.

When the mesh is such that y * < 11.225 at the wall-adjacent cells, the FV code applies the laminar stress-strain relationship that can be written as

* * U = y [B-53]

It should be noted that, in the FV code, the laws-of-the-wall for mean velocity and temperature are based on the wall unit, y * , rather than y + . These quantities are approximately equal in equilibrium turbulent boundary layers. y + is defined as:

+ ρu y y = τ [B-54] µ

B-2.5.2. Energy

Reynolds' analogy between momentum and energy transport gives a similar logarithmic law for mean temperature. As in the law-of-the-wall for mean velocity, the law-of-the-wall for temperature comprises the following two different laws:

• linear law for the thermal conduction sublayer where conduction is important

• logarithmic law for the turbulent region where effects of turbulence dominate conduction

The thickness of the thermal conduction layer is, in general, different from the thickness of the (momentum) viscous sublayer, and changes from fluid to fluid. For example, the thickness of the thermal sublayer for a high-Prandtl-number fluid (e.g., oil) is much less than its momentum sublayer thickness. For fluids of low Prandtl numbers (e.g., liquid metal), on the contrary, it is much larger than the momentum sublayer thickness.

The law-of-the-wall implemented in the FV code has the following composite form:

- 181 - Appendix B

1 1 ⎧ 4 2 1 C κ ⎪Pr y* + ρ Pr µ P U 2 ()y* < y * ⎪ 2 q P T 1 1 & 4 2 ⎪ * ()Tw − TP ρC pCµ κ P ⎪ ⎡ 1 * ⎤ T = = Prt ln()9.793y + P + [B-55] ⎨ ⎢κ ⎥ q& ⎪ ⎣ ⎦ 1 1 ⎪ C 4κ 2 ⎪ 1 µ P 2 2 * * ρ {}()Prt U P + ()Pr− Prt U c y > yT ⎩⎪ 2 q& where P is computed by using the formula given by Jayatilleke (1969):

3 4 ⎡ ⎤ −0.007Pr ⎛ Pr ⎞ ⎡ Pr ⎤ P = 9.24⎢⎜ ⎟ −1⎥ 1+ 0.28e t [B-56] ⎢⎜ Pr ⎟ ⎥⎣⎢ ⎦⎥ ⎣⎝ t ⎠ ⎦

* * The non-dimensional thermal sublayer thickness, yT , in Eq. [B-54] is computed as the y value at which the linear law and the logarithmic law intersect, given the molecular Prandtl number of the fluid being modeled.

The procedure of applying the law-of-the-wall for temperature is as follows. Once the physical properties of the fluid being modeled are specified, its molecular Prandtl number is computed. * Then, given the molecular Prandtl number, the thermal sublayer thickness, yT , is computed from the intersection of the linear and logarithmic profiles, and stored.

During the iteration, depending on the y * value at the near-wall cell, either the linear or the logarithmic profile in Eq. [B-55] is applied to compute the wall temperature or heat flux (depending on the type of the thermal boundary conditions).

B-2.5.3. Species

When using wall functions for species transport, the FV code assumes that species transport behaves analogously to heat transfer. Similarly to Eq. [B-54], the law-of-the-wall for species can be expressed for constant property flow with no viscous dissipation as

1 1 * * * 4 2 ⎧Sc y (y < yc ) * ()Yi,w − Yi ρCµ κ P ⎪ Y ≡ = ⎡ 1 ⎤ [B-57] ⎨Sc ln 9.793y* + P y* > y * J i,w ⎪ t ⎢ ()c ⎥ ()c ⎩ ⎣0.4187 ⎦

where Yi is the local species mass fraction, Sc and Sct are molecular and turbulent Schmidt * numbers, and J i,w is the diffusion flux of species i at the wall. Note that Pc and yc are * calculated in a similar way as P and yT (Eq. [B-56]), with the difference being that the Prandtl numbers are always replaced by the corresponding Schmidt numbers.

- 182 - Appendix B

B-2.5.4. Turbulence

In the κ −ε models the κ equation is solved in the whole domain including the wall-adjacent cells. The boundary condition for κ imposed at the wall is

∂κ = 0 [B-58] ∂n where n is the local coordinate normal to the wall.

The production of kinetic energy, Gκ , and its dissipation rate, ε , at the wall-adjacent cells, which are the source terms in the κ equation, are computed on the basis of the local equilibrium hypothesis. Under this assumption, the production of κ and its dissipation rate are assumed to be equal in the wall-adjacent control volume.

Thus, the production of κ is computed from

∂U τ w Gκ ≈ τ w = τ w 1 1 [B-59] ∂y 4 2 0.4187ρCµ κ P yP and ε is computed from

3 3 4 2 Cµ κ P ε P = [B-60] 0.4187yP

The ε equation is not solved at the wall-adjacent cells, but instead is computed using Eq. [B- 60].

Note that, as shown here, the wall boundary conditions for the solution variables, including mean velocity, temperature, species concentration, κ , and ε , are all taken care of by the wall functions. Therefore, you do not need to be concerned about the boundary conditions at the walls.

The standard wall functions work reasonably well for a broad range of wall-bounded flows. However, they tend to become less reliable when the flow situations depart too much from the ideal conditions that are assumed in their derivation. Among others, the constant-shear and local equilibrium hypotheses are the ones that most restrict the universality of the standard wall functions. Accordingly, when the near-wall flows are subjected to severe pressure gradients, and when the flows are in strong non-equilibrium, the quality of the predictions is likely to be compromised.

B-2.6. Convective heat and mass transfer modeling in the κ − ε models

Turbulent heat transport is modeled using the concept of Reynolds' analogy to turbulent momentum transfer. The "modeled'' energy equation is thus given by the following:

- 183 - Appendix B

∂ ∂ ∂ ⎛ ∂T ⎞ ()ρE + []u ()ρE + p = ⎜k + u ()τ ⎟ + S [B-61] i ⎜ eff i ij eff ⎟ h ∂t ∂xi ∂x j ⎝ ∂x j ⎠

k E τ where eff is the effective thermal conductivity, is the total energy, and ( ij )eff is the deviatoric stress tensor, defined by Eq. [B-18]

τ The term involving ( ij )eff represents the viscous heating. Additional terms may appear in the energy equation, depending on the physical models used. See Section 2.1.3 for more details.

For the standard and realizable κ −ε models, the effective thermal conductivity is given by

C p µt keff = k + [B-62] Prt where k is the thermal conductivity. The default value of the turbulent Prandtl number is 0.85.

For the RNG κ −ε model, the effective thermal conductivity is

keff = α C p µeff [B-63]

where α is calculated from Eq. [B-29], but with α 0 = 1/Pr = k / C p µ .

The fact that α varies with µ mol / µ eff , as in Eq. [B-29], is an advantage of the RNG κ −ε model. It is consistent with experimental evidence indicating that the turbulent Prandtl number varies with the molecular Prandtl number and turbulence (Kays, 1994). Eq. [B-29] works well across a very broad range of molecular Prandtl numbers, from liquid metals (Pr ≈ 10-2) to paraffin oils (Pr ≈ 103), which allows heat transfer to be calculated in low-Reynolds-number regions. Eq. [B-29] smoothly predicts the variation of effective Prandtl number from the molecular value (α = 1/Pr) in the viscosity-dominated region to the fully turbulent value (α = 1.393) in the fully turbulent regions of the flow.

Turbulent mass transfer is treated similarly. For the standard and realizable κ −ε models, the default turbulent Schmidt number is 0.7. For the RNG model, the effective turbulent diffusivity for mass transfer is calculated in a manner that is analogous to the method used for the heat transport. The value of α 0 in Eq. [B-29] is α 0 = 1/Sc, where Sc is the molecular Schmidt number.

B-3. The standard κ − ω model

The standard κ −ω model is an empirical model based on model transport equations for the turbulence kinetic energy (κ ) and the specific dissipation rate (ω ), which can also be thought of as the ratio of κ to ε (Wilcox, 1998a, 1998b).

- 184 - Appendix B

B-3.1. Transport equations

The turbulence kinetic energy,κ , and the specific dissipation rate,ω , are obtained from the following transport equations:

∂ ∂ ∂ ⎛ ∂κ ⎞ ρκ + ρ κ u = ⎜Γ ⎟ + G − Y + S () ()i ⎜ κ ⎟ κ κ κ [B-64] ∂t ∂xi ∂x j ⎝ ∂x j ⎠

∂ ∂ ∂ ⎛ ∂ϖ ⎞ ρω + ρ ω u = ⎜Γ ⎟ + G − Y + S () ()i ⎜ ω ⎟ ω ω ω [B-65] ∂t ∂xi ∂x j ⎝ ∂x j ⎠

In these equations, Gκ represents the generation of turbulence kinetic energy due to mean velocity gradients. Gϖ represents the generation of ϖ . Γκ and Γω represent the effective diffusivity of κ and ω , respectively. Yκ and Yω represent the dissipation of κ and ω due to turbulence. All of the above terms are calculated as described below. Sκ and Sω are user- defined source terms.

B-3.2. Modeling the effective diffusivity

The effective diffusivities for the κ −ω model are given by

µt Γκ = µ + [B-66] ()Prt κ

µt Γω = µ + [B-67] ()Prt ω

The turbulent viscosity is computed by combining κ and ω as follows:

* ρκ µ = α [B-68] t ω

B-3.2.1. Low-Reynolds-number correction

The coefficient α * damps the turbulent viscosity causing a low-Reynolds-number correction. It is given by

⎛ * Ret ⎞ ⎜α 0 + ⎟ * * R α = α ⎜ κ ⎟ [B-69] ∞ Re ⎜ 1+ t ⎟ ⎝ Rκ ⎠

- 185 - Appendix B

ρκ * β Re = ; R = 6; α = i ; β = 0.072; [B-70] t µω κ 0 3 i

* * Note that, in the high-Reynolds-number form of the κ −ω model, α = α ∞ = 1.

B-3.3. Modeling the turbulent production

B-3.3.1. Production of κ

The term Gκ represents the production of turbulence kinetic energy. It is defined in the same way as for the κ −ε model (see Eq. [B-48]).

B-3.3.2. Production of ω

The production of ω is given by

ω G = α G [B-71] ω κ κ

where Gκ is given by Eq. [B-48].

The coefficient α is given by

Re ⎛α + t ⎞ α ⎜ 0 R ⎟ α = ∞ ⎜ ω ⎟ [B-72] α * Re ⎜ 1+ t ⎟ ⎝ Rω ⎠

* where Rω = 2.95. α and Re t are given by Eqs. [B-69] and [B-70] respectively. Note that, in the high-Reynolds-number form of the κ −ω model, α = α ∞ = 1.

B-3.4. Modeling the turbulence dissipation

B-3.4.1. Dissipation of κ

The dissipation of κ is given by

* Yκ = ρβ f β * κω [B-73]

⎧1 X κ ≤ 0 ⎪ 2 f * = 1+ 680X [B-74] β ⎨ κ X > 0 ⎪ 2 κ ⎩1+ 400X κ

- 186 - Appendix B

1 ∂κ ∂ω X κ = 3 [B-75] ω ∂x j ∂x j

* * * β = β i [1+ ζ F()M t ] [B-76]

4 ⎛ ⎛Re ⎞ ⎞ ⎜ 4 + t ⎟ 15 ⎜ R ⎟ * * ⎜ ⎝ β ⎠ ⎟ β = β [B-77] i ∞ ⎜ 4 ⎟ ⎛Re ⎞ ⎜ 1+ t ⎟ ⎜ ⎜ R ⎟ ⎟ ⎝ ⎝ β ⎠ ⎠

* * ζ = 1.5; Rβ = 8; β ∞ = 0.09; [B-78]

where Re t and F()M t are given by Eqs. [B-70] and [B-83] respectively.

B-3.4.2. Dissipation of ω

The dissipation of ω is given by

2 Yω = ρ β f β ω [B-79]

1+ 70X ω f β = [B-80] 1+ 80X ω

Ωij Ω jkVki X ω = [B-81] * 3 ()β ∞ω

The strain rate tensor, Vij , is defined by Eq. [B-46]. The rotation rate tensor, Ω ij , is defined by Eq. [B-9]. Also,

* ⎡ β i * ⎤ β = β i ⎢1− ζ F()M t ⎥ [B-82] ⎣ β i ⎦

* β i and F()M t are defined by Eqs. [B-77] and [B-83], respectively.

B-3.4.3. Compressibility correction

The compressibility function, F()M t , is given by

⎧0 M t ≤ M t0 F()M t = ⎨ 2 2 [B-83] ⎩ M t − M t0 M t > M t0

- 187 - Appendix B

2 2κ M = ; M = 0.25; a = γRT [B-84] t a 2 t0

* * Note that, in the high-Reynolds-number form of the κ −ω model, β i = β ∞ . In the * * incompressible form, β = β i .

B-3.5. Model constants

* 1 * α ∞ = 1; α ∞ = 0.52; α 0 = ; β ∞ = 0.09; βi = 0.072; Rβ = 8; 9 [B-85] * Rκ = 6; Rω = 2.95; ζ = 1.5; M t0 = 0.25; σ κ = 2.0 σ ω = 2.0

B-3.6. Wall boundary conditions

The wall boundary conditions for the κ equation in the κ −ω models are treated in the same way as the equation is treated when enhanced wall treatments are used with the κ −ε models. This means that all boundary conditions for wall-function meshes will correspond to the wall function approach, while for the fine meshes, the appropriate low-Reynolds-number boundary conditions will be applied.

The value of ω at the wall is specified as

* 2 ρ()u + ω = ω [B-86] w µ

⎛ ⎞ + ⎜ + 6 ⎟ ω = min ωw , [B-87] ⎜ + 2 ⎟ ⎝ β i ()y ⎠

2 ⎧⎛ 50 ⎞ ⎪⎜ ⎟ + + κ s < 25 + ⎪⎜ ⎟ ⎝ κ s ⎠ ωw = ⎨ [B-88] 100 ⎪ κ + ≥ 25 ⎪ + s ⎩ κ s

⎛ ρκ u * ⎞ + max⎜1.0, s ⎟ κ s = ⎜ ⎟ [B-89] ⎝ µ ⎠

+ and k s is the roughness height. In the logarithmic (or turbulent) region, the value of ω is

+ + 1 du ω = turb [B-90] * dy + β ∞

- 188 - Appendix B

which leads to the value of ω in the wall cell as

u * ω = [B-91] * β ∞ κy

References

Choudhury, D., Kim, S.-E., Flannery, W.S., (1993). Calculation of turbulent separated flows using a renormalization group based k-ε turbulence model. American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FED 149, 177 – 187.

Jayatilleke, C., (1969). The Influence of Prandtl number and surface roughness on the resistance of the laminar sublayer to momentum and heat transfer. Progress in Heat and Mass Transfer, 1, 193 – 321.

Kays, W.M., (1994). Turbulent Prandtl number. Where are we?. Journal of Heat Transfer, 116, 284 – 295.

Launder, B.E. and Spalding, D.B., (1972). Lectures in mathematical models of turbulence. Academic Press, London.

Launder, B.E. and Spalding, D.B., (1974). The numerical computation of turbulent flows. Computer Methods in Applied Mechanics and Engineering, 3, 269 – 289.

Reynolds, W.C., (1987). Fundamentals of turbulence for turbulence modeling and simulation. Lecture notes for Von Karman Institute, agard report No. 755.

Shih, T.-H., Liou, W.W., Shabbir, A., Yang, Z., Zhu, J., (1995). New k-ε eddy viscosity model for high Reynolds number turbulent flows. Computers and Fluids, 24, 227 – 238.

Spalart, P. and Allmaras, S., (1992). A one-equation turbulence model for aerodynamic flows. Technical Report AIAA-92-0439, American Institute of Aeronautics and Astronautics.

Wilcox, D.C., (1998a). Reassessment of the scale-determining equation for advanced turbulence models. AIAA Journal, 26, 1299 – 1310.

Wilcox, D.C., (1998b). Multiscale model for turbulent flows. AIAA Journal, 26, 1311 – 1320.

- 189 -

APPENDIX C

DIMENSIONLESS NUMBERS Physical meaning and applied formulas

Harmonic works, « Dimensionless travels »

Appendix C

C-1. Biot number

The Biot number is a dimensionless number used in unsteady-state (or transient) heat transfer calculations. It is named after the French physicist Jean Baptiste Biot (1774- 1862), and relates the heat transfer resistance inside and at the surface of a body.

The Biot number can be expressed as:

h ⋅ L Bi = w [C-1] k r Figure C-1. Jean Baptiste Biot

C-2. Eckert number

The Eckert number is a dimensionless number used in flow calculations. It expresses the relationship between a flow's kinetic energy and enthalpy, and is used to characterize dissipation. It is named for the late professor Ernst R. G. Eckert (1904 – 2004), known for his work with the early development of jet engines and for discovering ways to increase rocket efficiency.

The Eckert number can be expressed as:

u 2 Ec = [C-2] Figure C-2. Ernst R. G. Eckert C p ∆T

C-3. Euler number

The Euler number is a dimensionless value common used for analyzing fluid flow dynamics problems where the pressure difference between two points is an important variable. The Euler Number can be interpreted as a measure of the ratio of pressure forces to inertial forces. It is named for Leonhard Euler (1707-1783), mathematician who first explained role of pressure in fluid flow, formulated basic equations of motion and so-called Bernoulli theorem and introduced the concept of cavitation and the principles of centrifugal machinery.

The Euler number can be expressed as:

Figure C-3. Leonhard Euler p Eu = [C-3] ρ u 2

- 193 - Appendix C

C-4. Froude number

The Froude number is a dimensionless parameter measuring of the ratio of the inertia force on an element of fluid to the weight of the fluid element - the inertial force divided by gravitational force. It is named for William Froude (1810 – 1879), engineer, hydrodynamicist and naval architect who was the first to formulate reliable laws for the resistance that water offers to ships and for predicting their stability.

The Froude number can be expressed as:

u Fr = [C-4] Figure C-4. William Froude g D p

C-5. Grashof number

The Grashof number is a dimensionless number in fluid dynamics which approximates the ratio of the buoyancy force to the viscous force acting on a fluid. It is named after the German engineer Franz Grashof (1826 – 1893).

The Grashof number can be expressed as:

3 gβ (Ts − T∞ )D p Gr = [C-5] H ν 2

There is an analogous form of the Grashof number used in cases of natural convection mass transfer problems:

Figure C-5. Franz Grashof 3 2 gD p ∆ρ ⎛ ρ ⎞ GrD = ⎜ ⎟ [C-6] ρ ⎝ µ ⎠

C-6. Mach number

The Mach number is defined as a ratio of the speed of flow relative to the speed of sound in the flowing fluid. It can be shown that the Mach number is also the ratio of inertial forces (also referred to aerodynamic forces) to elastic forces.

The Mach number is named after Austrian physicist and philosopher Ernst Mach (1838 – 1916), an can be expressed as:

u Ma = [C-7] usound Figure C-6. Ernst Mach

- 194 - Appendix C

C-7. Nusselt number

The Nusselt number is a dimensionless number which measures the enhancement of heat transfer from a surface which occurs in a 'real' situation, compared to the heat transfer that would be measured if only conduction could occur. Typically it is used to measure the enhancement of heat transfer when convection takes place. It is named after the German engineer Wilhelm Nusselt (1882 – 1957), pioneer in the study of the basic laws of heat transfer, and can be expressed as:

hD p Nu = [C-8] Figure C-7. Wilhelm Nusselt k f

C-8. Péclet number

In physics, the Péclet number is a dimensionless number relating the forced convection of a system to its heat conduction. It is equivalent to the product of the Reynolds number with the Prandtl number. It is named after the French Physicist Jean Claude Eugene Péclet (1793 – 1857)

There are various definitions of the Péclet number. The most typical are as follows:

D puC p ρ Pe = = Re ⋅ Pr [C-9] Figure C-8. Jean Claude k f Eugene Péclet

C-9. Prandtl number

The Prandtl Number is a dimensionless number approximating the ratio of momentum diffusivity and thermal diffusivity. It is named after Ludwig Prandtl (1875 – 1953), German physicist famous for his works in aeronautics.

The Prandtl number can be expressed as:

C p µ Pr = [C-10] k f Figure C-9. Ludwig Prandtl

- 195 - Appendix C

C-10. Rayleigh number

In fluid mechanics, the Rayleigh number for a fluid is a dimensionless number associated with the heat transfer within the fluid. When the Rayleigh number is below the critical value for that fluid, heat transfer is primary in the form of conduction; when it exceeds the critical value, heat transfer is primarily in the form of convection.

The Rayleigh number is named after John William Strutt, 3rd Baron of Rayleigh (aka Lord Rayleigh, 1842 – 1919) and is defined as the product of the Grashof number and the Prandtl number.

3 2 D p ρ gβC p ∆T Figure C-10. Lord Rayleigh Ra = = Gr ⋅ Pr [C-11] k f µ

C-11. Reynolds number

The Reynolds number is the ratio of inertial forces to viscous forces and is used for determining whether a flow will be laminar or turbulent. It is the most important dimensionless number in fluid dynamics and provides a criterion for determining dynamic similitude.

It is named after the Irish fluid dynamics engineer Osbourne Reynolds (1842 – 1912), who proposed it in 1883. Typically it is given as follows for flow through a packed bed:

D puρ Figure C-11. Osbourne Re = [C-12] Reynolds µ

C-12. Schmidt number

The Schmidt number is a dimensionless number approximating the ratio of momentum diffusivity and mass diffusivity, and is used to characterize fluid flows in where there are simultaneous momentum and mass diffusion convection processes. It is named after Ernst Schmidt (1892 – 1975), German scientist, pioneer in the field of engineering thermodynamics, especially in heat and mass transfer.

The Schmidt number is the mass-transfer equivalent of the Prandtl number, and it can be expressed as:

µ Sc = [C-13] Figure C-12. Ernst Schmidt ρDAB

- 196 - Appendix C

C-13. Sherwood number

The Sherwood number is a dimensionless number used in mass-transfer operation. It represents the ratio of length scale to the diffusive boundary layer thickness. It is named after Thomas Kilgore Sherwood (1903 – 1976), American chemical engineer whose primary research area was mass transfer and its interaction with flow and with chemical reaction.

The Sherwood number is the mass-transfer equivalent of the Nusselt number, and it can be expressed as:

kc D p Figure C-13. Thomas Kilgore Sh= [C-14] Sherwood DAB

C-14. Stanton number

The Stanton number is a dimensionless number which measures the ratio of heat transferred into a fluid to the thermal capacity of fluid. It is used to characterize heat transfer in forced convection flows.

The Stanton number is named after Sir Thomas Eduard Stanton (1865 – 1931). Stanton's main field of interest was fluid flow and friction, and the related problem of heat transmission.

h Nu St = = [C-15] Figure C-14. Sir Thomas C p ρ u Re ⋅ Pr Eduard Stanton

C-15. Strouhal number

The Strouhal Number is a dimensionless value useful for analyzing oscillating, unsteady fluid flow dynamics problems. It represents a measure of the ratio of inertial forces due to the unsteadiness of the flow or local acceleration to the inertial forces due to changes in velocity from one point to an other in the flow field.

The Strouhal number, named after the Czech professor of experimental physics Čeněk Strouhal (1850 – 1922), is proportional to the reciprocal of vortex spacing expressed as number of obstacle diameters and is used in momentum transfer in general and Von Karman vortex streets and unsteady state flow calculations in particular. It is defined in Figure C-15. Čeněk Strouhal the following form:

ωD p Sr = [C-16] u

- 197 - Appendix C

C-16. Turbulent numbers

In the development of the dimensionless analysis there appears variations of some of the aforementioned dimensionless numbers when turbulent flow is considered (i.e., Reynolds, Prandtl and Schmidt turbulent numbers). In these cases, applied formulas to estimate this numbers are as follows:

D puρ Ret = [C-17] µt

C p µt Prt = [C-18] kt

µt Sct = [C-19] ρDAB

Turbulence intensity used to estimate µt was set between 1 - 4 %, and Reynolds analogy was used to estimate kt from µt (White, 1991). For first analysis purposes, Prt was assumed as a constant value within the bed, justified in the fact that majority of experimental results shows a range of variations between 0.75 < Prt < 2 for air and water (Kays, 1994).

References

Kays, W.M., (1994). Turbulent Prandtl number. Where are we? Journal of Heat Transfer, 116, 284 – 295.

White, F., (1991). Viscous Fluid Flow; McGraw Hill, New York, pp. 482 – 542.

- 198 - APPENDIX D

FLUID PROPERTIES ESTIMATION & CFD IMPLEMENTATION

Jodie Coston, « Green background »

Appendix D

D-1. Density

D-1.1. Gas @ atmospheric pressure

In the case of a gas at atmospheric pressure it was chosen to define the density using the ideal gas law for an incompressible flow, which is implemented by default in the FV code (Fluent Inc., 2005). In this case, the density is computed as:

pop M w ρ = [D-1] RT where R is the universal gas constant, Mw is the molecular weight of the gas and pop is the operating pressure. In this form, the density depends only on the operating pressure and not on the local relative pressure field.

D-1.2. Fluid @ P ≥ Pc

In the case of a supercritical fluid it was chosen to define the density using a cubic equation of state (EOS). In this case the density is computed as:

pop M w ρ = [D-2] zRT where z is the compressibility factor obtained from solving a cubic EOS, which in general form can be expressed as:

3 * * 2 * *2 * *2 * * *2 *3 z − (1+ B − uB )z + (A + wB − uB − uB )z − A B − wB − wB = 0 [D-3] where:

* aP A = [D-4] R 2T 2 and

* bP B = [D-5] RT

Two cubic EOS were used to estimate the density of a fluid in supercritical conditions during the development of this thesis. Redlich-Kwong EOS (Redlich and Kwong, 1949) was used for estimating the density of a pure substance, and Peng-Robinson EOS (Peng and Robinson, 1976) was applied for estimating the density of a mixture. For these equations, a, b, u and w take the values listed in Table D-1.

- 201 - Appendix D

Equation u w b a

2 2.5 Redlich- 0.08664RTc 0.42748R Tc 1 0 Kwong 0.5 Pc PcT 2 2 2 ⎡ ⎛ 0.5 ⎞⎤ 0.45724R Tc ⎜ ⎛ T ⎞ ⎟ Peng- 0.07780RTc ⎢1+ fω 1− ⎜ ⎟ ⎥ 2 -1 ⎜ ⎜ ⎟ ⎟ Robinson Pc ⎢ ⎝ Tc ⎠ ⎥ Pc ⎣ ⎝ ⎠⎦ 2 fω = 0.37464 +1.54226ω − 0.26992ω Table D-1. Constants for the cubic equations of state used

As commercial CFD codes do not offer real-gas equations of state within their default equation system, these equations have to be created and uploaded to the CFD solver if a supercritical fluid has to be modelled. In order to implement the aforementioned EOS within the CFD solver, user-defined functions (UDF) and user-defined equations (UDE) were used.

For the UDF creation, an analytical solution of the EOS was compiled using a build-up C++ compiler (incorporated within the CFD software), and included within the equation system of the CFD solver. This strategy allowed the CFD code to compute the density of the fluid as a function of the static pressure and the temperature in every point of the computational grid. An example of a UDF for estimating the density of CO2 using the Redlich-Kwong EOS would be as follows:

%UDF EXAMPLE% %PROPERTY TO BE MODIFIED INTO SOLVER: DENSITY% %FLUID: CO2% %Tr: 304.1 K% %Pr: 7380000 Pa% %EOS: REDLICH-KWONG%

#include "udf.h"

DEFINE_PROPERTY(cell_density, cell, thread) { real ro; real a; real b; real z; real tr; real pr; real a2; real b2; real r; real q; real f; real g; real c; real d; real e; real fi; real z1; real z2; real z3; real t=C_T(cell, thread);

- 202 - Appendix D

real p=C_P(cell, thread);

tr=t/304.1; pr=p/7380000.; a=6456696.46; b=0.02968; a2=0.42747*pr/pow(tr,(5/2)); b2=0.08664*pr/tr; r=a2*b2; q=pow(b2,2.)+b2-a2; f=((-3.*q)-1.)/3.; g=((-27.*r)-(9.*q)-2.)/27.; c=pow((f/3.),3.)+pow((g/2.),2.); if (c>0.0) { d=pow(((-g/2.)+pow(c,0.5)),(1./3.)); e=pow(((-g/2.)-pow(c,0.5)),(1./3.)); z=d+e+(1./3.); } else { fi=a*cos(-6.75*pow(g,2.)/pow(f,3.)); z1=(2*pow((-f/3.),0.5)*cos(fi/3.))+(1/3); z2=(2*pow((-f/3.),0.5)*cos((fi/3.)+2.0944))+(1./3.); z3=(2*pow((-f/3.),0.5)*cos((fi/3.)+4.1888))+(1./3.); if (z1>z2) { if (z1>z3) z=z1; else z=z3; } else { if (z2>z3) z=z2; else z=z3; } } ro=44.*p/(8314.*z*t);

return ro; }

For situations where the static pressure could be considered as constant along the computed model, a simplified density model could be used. This simplified model consisted on expressing the density of the fluid (at constant pressure) as a function of temperature, fitting the results obtained with the EOS to a 6th degree polynomial expression. The obtained polynomial expression (which can be written in general form as shown in Eq. [D-6]), can then be fed into the CFD solver as an UDE.

6 5 4 3 2 ρ = aT + bT + cT + dT + eT + fT + g [D-6]

A sample of the obtained coefficients for Eq. [D-6] for estimating the density of supercritical CO2 can be seen in Table D-2, and a graphical representation of the previously stated polynomial fitting is shown in Figure D-1. In order to quantify the round-up error associated

- 203 - Appendix D

with the numerical solver, a simulation for a density varying flow of supercritical CO2 in a Venturi tube was set, and obtained numerical values were compared with the prediction of the EOS, resulting in a good agreement between the numerical results and the EOS estimation (see Figure D-2).

P [MPa] a b c d e f g 8 1.13 x 10-9 -2.65 x 10-6 2.58 x 10-3 -1.33 x 100 3.87 x 102 -5.98 x 104 3.84 x 106 10 -4.05 x 10-10 8.77 x 10-7 -7.80 x 10-4 3.63 x 10-1 -9.30 x 101 1.23 x 104 -6.48 x 105 12 -1.44 x 10-9 3.34 x 10-6 -3.22 x 10-3 1.65 x 100 -4.72 x 102 7.17 x 104 -4.52 x 106 14 -4.78 x 10-10 1.14 x 10-6 -1.14 x 10-3 6.01 x 10-1 -1.78 x 102 2.78 x 104 -1.81 x 106 16 6.61 x 10-11 -1.22 x 10-7 8.58 x 10-5 -2.70 x 10-2 2.75 x 100 3.46 x 102 -6.86 x 104 18 1.79 x 10-10 -3.98 x 10-7 3.65 x 10-4 -1.77 x 10-1 4.77 x 101 -6.78 x 103 3.99 x 105 20 1.43 x 10-10 -3.24 x 10-7 3.04 x 10-4 -1.51 x 10-1 4.18 x 101 -6.12 x 103 3.72 x 105 22 8.89 x 10-11 -2.04 x 10-7 1.94 x 10-4 -9.76 x 10-2 2.73 x 101 -4.05 x 103 2.50 x 105

Table D-2. Obtained coefficients for Eq. [D-6] for supercritical CO2

850

700 ] 3

550

400 Density [kg/m Density 250

100 310 330 350 370 390 410 430 450 Temperature [K] 8 MPa 10 MPa 12 MPa 14 MPa 16 MPa 18 MPa 20 MPa 22 MPa

Figure D-1. Density polynomial fitting for supercritical CO2

255

250

245 from CFD ] 3 240 [kg/m 235 ρ

230 230 235 240 245 250 255

ρ [kg/m3] from EOS

Figure D-2. Density parity plot for supercritical CO2 (numerical results vs. EOS)

- 204 - Appendix D

D-1.3. Mixtures

In order to study how the density of a mixture was estimated by the CFD software, a 2- dimensional cylinder of 5 m length and 0.2 m diameter was created. From one side of the tube CO2 in laminar flow regime was fed to the model. In order to impose a density gradient along the tube, a flux of 0.01 kg/s of toluene vapor was imposed through the pipe walls. Three temperatures (i.e. 298, 310 and 320 K) and four pressures (i.e. 0.1, 6, 8 and 20 MPa) were taken as study cases. For atmospheric pressure situations, incompressible ideal gas law was used to estimate density. For near-critical and supercritical conditions, Peng Robinson equation of state for density was implemented through a C++ subroutine inside the CFD solver. For each of the simulations it was recorded the C7H8 mass fraction together with the mixture density at the central axis of the tube. The numerical results obtained were compared against the estimation through equations of state (Guardo et al., 2005).

Low pressure density was determined by ideal gas law for both of the pure components assuming the flow as incompressible. In such way the density is computed by the CFD solver as shown in Eq. [D-7].

P ρ = [D-7] y R ⋅T ⋅ ∑ i M w

It was expected that for low pressure both methods would give similar results due to the low interaction forces between the molecules. The results obtained at the different temperatures tested (i.e. 298, 310 and 320K) are very similar to the ones shown in Figure D-3. From those results it was checked that the numerical error of the CFD solver when applying the ideal gas law was neglectable.

1.95 ] 3 1.9

1.85

1.8 Mixture density[kg/m

1.75 0 0.02 0.04 0.06 0.08 0.1

Mass fraction C7H8 EOS Estimation CFD Estimation

Figure D-3. Density estimation for a binary mixture [C7H8/CO2] at 298 K and 0.1 MPa

In order to study high pressure situations, estimation of the mixture density was checked at 6, 8 and 20 MPa. Peng-Robinson EOS was implemented via UDF in order to obtain the compressibility factors. In such way the density is computed by the CFD solver as shown in Eq. [D-8].

- 205 - Appendix D

P ρ = [D-8] y z ⋅ R ⋅T ⋅ ∑ i M w

An example of the results obtained for high pressure situations can be seen in Figure D-4. In general, for all situations simulated, it was noticed that despite the fact that the estimation done by the CFD solver is close to the EOS prediction for low solute mass fractions due to the good pure component density estimation, such a simple mixing rule is not able to predict the mixture critical point or phase changes. For all cases analyzed the divergence between CFD estimated results and the EOS prediction increases as the mass fraction of solute increases. According to this, the greatest numerical errors obtained for the CFD simulations were located at the saturation point (equilibrium conditions at the specified pressure and temperature). Maximum error measured when estimating density was of 6.24%, which is an acceptable value.

300 ] 3 275 [kg/m

250

225 Mixture density

200 0 0.003 0.006 0.009 0.012

Mass fraction C7H8

EOS estimation CFD Estimation

Figure D-4. Density estimation for a binary mixture [C7H8/CO2] at 320 K and 8 MPa

D-2. Viscosity

D-2.1. Gas @ atmospheric pressure

In the case of a gas at atmospheric pressure it was chosen to define the density using a viscosity power law, which is implemented by default in the FV code (Fluent Inc., 2005). In this case, the viscosity is computed as:

n ⎛ T ⎞ ⎜ ⎟ µ = µ0 ⎜ ⎟ [D-9] ⎝ T0 ⎠

where T0 and µ0 are reference values for temperature and viscosity, respectively. Table D-3 shows the values of the coefficients involved in Eq. [D-9] for CO2 and air at moderated pressures and temperatures.

- 206 - Appendix D

Fluid µ0 [Pa· s] T0 [K] n

-5 CO2 1.37 x 10 273.11 0.79

Air 1.716 x 10-5 273.11 0.666

Table D-3. Viscosity power law coefficients for CO2 and air

D-2.2. Fluid @ P ≥ Pc

In the case of a supercritical fluid it was chosen to define the viscosity following the procedure recommended by Lucas (1980), which first calculates the parameters Z1 and Z2 for the reduced temperature of interest:

0.618 −0.449Tr −4.058Tr 0 0 0 Z1 = (0.807Tr − 0.357e + 0.340e + 0.018)FP FQ = µ ξ [D-10]

0 0 where FP and FQ are low pressure polarity and quantum correction factors determined as shown in Eqs. [D-12] and [D-13] respectively. To obtain the first of the aforementioned correction factors, a reduced dipole moment is required. Lucas defines this quantity as

2 −29 σ Pc σ r = 1.75216×10 2 [D-11] Tc

0 Then, FP values are found as:

0 FP = 1 0 ≤ σ r < 0.022 0 1.72 FP = 1+ 30.55()0.292 − zc 0.022 ≤ σ r < 0.075 [D-12] 0 1.72 FP = 1+ 30.55()0.292 − zc 0.96 + 0.1(Tr − 0.7) 0.075 ≤ σ r

0 The factor FQ is used only for the quantum gases He, H2 and D2.

1 2 0 0.15 ⎧ M w ⎫ FQ = 1.22Q ⎨1+ 0.00385[]()Tr −12 Tr −12 ⎬ [D-13] ⎩ ⎭

0 Where Q = 1.38 for He, 0.76 for H2 and 0.52 for D2. For non-quantum gases, FQ = 1.

If (1 < Tr < 40) and (0 < PR ≤ 100), then

⎡ e ⎤ 0 aPr Z 2 = µ ξ ⎢1+ ⎥ [D-14] f d −1 ⎣⎢ bPr + ()1+ cPr ⎦⎥

- 207 - Appendix D

0 where µ ξ is found from Eq. [D-10]. The values of the constants in Eq. [D-14] are defined as follows:

−3 1.245×10 −0.3286 a = e5.1726Tr Tr

b = a()1.6553Tr −1.2723

0.4489 −37.7332 c = e3.0578Tr Tr [D-15]

1.7368 −7.6351 d = e 2.2310Tr Tr e = 1.3088

0.4489 f = 0.9425e −0.1853Tr

After computing Z1 and Z2, we define

Z Y = 2 [D-16] Z1

and the correction factors FP and FQ,

0 −3 1+ (FP −1)Y FP = 0 [D-17] FP

0 −1 4 1+ ()FQ −1 [Y − 0.007()lnY ] FQ = 0 [D-18] FQ

Finally, the dense gas viscosity is calculated as

Z 2 FP FQ µ = [D-19] ξ where

1 ⎛ T ⎞ 6 ξ = 0.176⎜ c ⎟ [D-20] ⎜ 3 4 ⎟ ⎝ M w Pc ⎠

The aforementioned method for viscosity estimation was implemented into the CFD solver via UDF. An example of a UDF for estimating the viscosity of CO2 using the Lucas method would be as follows:

- 208 - Appendix D

%UDF EXAMPLE% %PROPERTY TO BE MODIFIED INTO SOLVER: VISCOSITY% %FLUID: CO2% %Tr: 304.1 K% %Pr: 7380000 Pa% %METHOD: LUCAS%

#include "udf.h"

DEFINE_PROPERTY(cell_viscosity, cell, thread) { real mu; real a; real b; real c; real d; real e; real f; real mu_lp; real tr; real pr; real t=C_T(cell, thread); real p=C_P(cell, thread);

tr=t/304.1; pr=p/7380000; a=(0.001245/tr)*exp(5.1726*pow(tr,-0.3286)); b=a*((1.6553*tr)-1.2723); c=(0.4489/tr)*exp(3.0578*pow(tr,-37.7332)); d=(1.7368/tr)*exp(2.2310*pow(tr,-7.6351)); e=1.3088; f=0.9425*exp(-0.1853*pow(tr,0.4489)); mu_lp=((0.807*pow(tr,0.618))-(0.357*exp(-0.449*tr))+(0.340*exp(- 4.058*tr))+0.018)/39194.92; mu=mu_lp*(1+((a*pow(pr,e))/((b*pow(pr,f))+pow((1+(c*pow(pr,d))), -1))));

return mu; }

For situations where the static pressure could be considered as constant along the computed model, a simplified viscosity model could be used. This simplified model consisted on expressing the viscosity of the fluid (at constant pressure) as a function of temperature, fitting the results obtained with the theoretical method to a 6th degree polynomial expression. The obtained polynomial expression (which can be written in general form as shown in Eq. [D-21]), can then be fed into the CFD solver as an UDE.

6 5 4 3 2 µ = aT + bT + cT + dT + eT + fT + g [D-21]

A sample of the obtained coefficients for Eq. [D-21] for estimating the viscosity of supercritical CO2 can be seen in Table D-4, and a graphical representation of the previously stated polynomial fitting is shown in Figure D-5. In order to quantify the round-up error associated with the numerical solver, a simulation for a viscosity-varying flow of supercritical CO2 in a Venturi tube was set, and obtained numerical values were compared with the prediction of the Lucas method, resulting in a good agreement between the numerical results and the theoretical estimation (see Figure D-6).

- 209 - Appendix D

P [MPa] a b c d e f g

8 2.22 x 10-18 -5.27 x 10-15 5.21 x 10-12 -2.74 x 10-9 8.07 x 10-7 -1.26 x 10-4 8.23 x 10-3 10 -1.01 x 10-18 2.10 x 10-15 -1.78 x 10-12 7.80 x 10-10 -1.84 x 10-7 2.18 x 10-5 -9.52 x 10-4 12 -4.11 x 10-18 9.30 x 10-15 -8.72 x 10-12 4.33 x 10-9 -1.20 x 10-6 1.77 x 10-4 -1.07 x 10-2 14 -4.77 x 10-18 1.09 x 10-14 -1.03 x 10-11 5.17 x 10-9 -1.45 x 10-6 2.16 x 10-4 -1.33 x 10-2 16 -4.18 x 10-18 9.57 x 10-15 -9.08 x 10-12 4.57 x 10-9 -1.28 x 10-6 1.91 x 10-4 -1.18 x 10-2 18 -3.32 x 10-18 7.60 x 10-15 -7.19 x 10-12 3.60 x 10-9 -1.01 x 10-6 1.49 x 10-4 -9.12 x 10-3 20 -2.52 x 10-18 5.71 x 10-15 -5.36 x 10-12 2.66 x 10-9 -7.34 x 10-7 1.07 x 10-4 -6.41 x 10-3 22 -1.75 x 10-18 3.89 x 10-15 -3.57 x 10-12 1.72 x 10-9 -4.61 x 10-7 6.47 x 10-5 -3.67 x 10-3

Table D-4. Obtained coefficients for Eq. [D-21] for supercritical CO2

9.E-05 [Pa· s] 6.E-05 Viscosity Viscosity

2.E-05 310 330 350 370 390 410 430 450 Temperature [K] 8 MPa 10 MPa 12 MPa 14 MPa 16 MPa 18 MPa 20 MPa 22 MPa

Figure D-5. Viscosity polynomial fitting for supercritical CO2

2.16E-05

2.15E-05 from CFD [Pa· s]

2.14E-05 Viscosity Viscosity

2.13E-05 2.13E-05 2.14E-05 2.15E-05 2.16E-05

Viscosity [Pa· s] from estimation

Figure D-6. Viscosity parity plot for supercritical CO2 (numerical results vs. Lucas method)

- 210 - Appendix D

D-2.3. Mixtures

To estimate the viscosity of a mixture (at low or high pressure), a similar test to that described in section D-1.3. was developed. In this test, the pure component viscosity was calculated by the aforementioned method of Lucas (1980), imposed to the CFD solver via UDF or UDE. For estimating the viscosity of the mixture, a mass weighted mixing rule was applied:

µ = ∑Yi ⋅ µi [D-22] i

Results obtained with the CFD solver were compared against those obtained using Lucas Method for mixtures (i.e., see Figure D-7). The results are almost identical for the three temperatures tested in atmospheric pressure situations. From Figure D-7 it can be seen that the calculations through CFD are almost exactly the same as the ones done with the contrast method, proving as done for the density that the CFD solver fits the results expected with the selected methods for comparison.

1.57E-05

1.53E-05 [Pa· s] [Pa·

1.49E-05 Viscosity

1.45E-05 0 0.025 0.05 0.075 0.1

Mass fraction C7H8 Lucas estimation CFD estimation

Figure D-7. Viscosity estimation for a binary mixture [C7H8/CO2] at 310 K and 0.1 MPa 1.E-03 [Pa· s] 1.E-04 Viscosity

1.E-05 0 0.025 0.05 0.075 0.1

Mass fraction C7H8

CFD Estimation Lucas Estimation

Figure D-8. Viscosity estimation for a binary mixture [C7H8/CO2] at 298 K and 60 MPa

- 211 - Appendix D

An example of the results obtained for the estimation of viscosity in high pressure situations can be seen in Figure D-8. As happened with density, for all cases analyzed the divergence between CFD estimated results and Lucas estimation increases as the mass fraction of solute increases. According to this, the greatest numerical errors obtained for the CFD simulations were located at the saturation point. Measured errors were lower than 10 %, which is an acceptable range (Guardo et al., 2005).

D-3. Thermal conductivity

D-3.1. Gas @ atmospheric pressure

In the case of a gas at atmospheric pressure it was chosen to define the thermal conductivity as a constant (independent of pressure and temperature). The values used for the thermal conductivities of the simulated gases were taken from experimental data and specific correlations available in the literature (Reid et al., 1987; Yaws, 1999).

D-3.2. Fluid @ P ≥ Pc

In the case of a supercritical fluid it was chosen to define the thermal conductivity using excess thermal conductivity correlations (Stiel and Thodos, 1964).

0 5 −2 0.535ρr (k − k )Γzc = 1.22×10 (e −1) ρ r < 0.5 [D-23]

0 5 −2 0.67ρr (k − k )Γzc = 1.14×10 (e −1.069) 0.5 < ρ r < 2.0 [D-24]

0 5 −3 1.155ρr (k − k )Γzc = 2.60×10 (e + 2.016) 2.0 < ρ r < 2.8 [D-25] where

1 3 2 6 ⎡Tc M w N 0 ⎤ Γ = ⎢ 5 4 ⎥ [D-26] ⎣ R Pc ⎦

The aforementioned method for thermal conductivity estimation was implemented into the CFD solver via UDF. An example of a UDF for estimating the thermal conductivity of CO2 using the thermal excess method would be as follows:

%UDF EXAMPLE% %PROPERTY TO BE MODIFIED INTO SOLVER: THERMAL CONDUCTIVITY% %FLUID: CO2% %Tr: 304.1 K% %Pr: 7380000 Pa% %METHOD: THERMAL EXCESS%

#include "udf.h"

DEFINE_PROPERTY(cell_thermal_conductivity, cell, thread) { real ro=C_R(cell, thread);

- 212 - Appendix D

real t=C_T(cell, thread); real ko; real k; real ror;

ror=ro/468.58; ko=-0.007215+(0.00008015*t)+(0.000000005477*pow(t,2.)- (0.00000000001053*pow(t,3.); if (ror<0.5) k=(0.0385*(exp(0.535*ror)-1))+ko; else if (ror<2.) k=(0.036*(exp(0.67*ror)-1.069))+ko; else if (ror<2.8) k=(0.0082*(exp(1.155*ror)+2.016))+ko;

return k; }

In order to quantify the round-up error associated with the numerical solver, a simulation for a thermal conductivity-varying flow of supercritical CO2 in a Venturi tube was set, and obtained numerical values were compared with the prediction of the thermal excess method, resulting in a good agreement between the numerical results and the theoretical estimation (see Figure D-9).

0.0337 from

0.0335 [W/m· K]

0.0333 CFD

0.0331

Thermal conductivity 0.0329 0.0329 0.0331 0.0333 0.0335 0.0337

Thermal conductivity [W/m· K] from estimation

Figure D-9. Thermal conductivity parity plot for supercritical CO2 (numerical results vs. thermal excess method)

D-4. Heat capacity

D-4.1. Gas @ atmospheric pressure

In the case of a gas at atmospheric pressure it was chosen to define the heat capacity as a constant (independent of pressure and temperature). The values used for the heat capacity of the simulated gases were taken from experimental data and specific correlations available in the literature (Reid et al., 1987; Yaws, 1999).

- 213 - Appendix D

D-4.2. Fluid @ P ≥ Pc

In the case of a supercritical fluid it was chosen to define the heat capacity relating it to the value in the ideal-gas state, at the same temperature and composition:

0 C p = C p + ∆C p [D-27]

where this relation applies to either a pure gas or a mixture at constant composition. ∆C p is a residual heat capacity. For a pressure-explicit equation of state, ∆C p is most conveniently determined by (see Modell and Reid, 1983):

V ⎛ ∂ 2 P ⎞ T ()∂P ∂T 2 ∆C = T ⎜ ⎟dV − V − R [D-28] p ∫ ⎜ 2 ⎟ ∞ ⎝ ∂T ⎠ ()∂P ∂V T

The aforementioned method for the heat capacity estimation was implemented into the CFD solver via UDF. An example of a UDF for estimating the heat capacity of CO2 using the thermal excess method would be as follows:

%UDF EXAMPLE% %PROPERTY TO BE MODIFIED INTO SOLVER: HEAT CAPACITY% %FLUID: CO2% %Tr: 304.1 K% %Pr: 7380000 Pa% %METHOD: RESIDUAL HEAT CAPACITY - EOS%

#include "udf.h"

DEFINE_PROPERTY(cell_heat_capacity, cell, thread) { real ro=C_R(cell, thread); real t=C_T(cell, thread); real co; real dc; real c; real a; real b; real c; real d; real e; real f; real g; real h; real v;

v=0.044/ro; co=19.8+(0.07344*t)-(0.00005602*pow(t,2.)) +(0.00000001715*pow(t,3.)); a=6.46; b=0.0000297; c=0.75*a/(b*pow(t,1.5)); d=log((v+b)/v); e=8.314/(v-b); f=a/(2*v*(v+b)*pow(t,1.5)); g=a*((2*v)+b)/(pow(t,0.5)*pow(v,2.)*pow((v+b),2.));

- 214 - Appendix D

h=8.314*t/pow((v-b),2); dc=(c*d)-(t*pow((e+f),2.)/(g-h))-8.314; c=(co+dc)/0.044;

return c; }

For situations where the static pressure could be considered as constant along the computed model, a simplified heat capacity model could be used. This simplified model consisted on expressing the heat capacity of the fluid (at constant pressure) as a function of temperature, fitting the results obtained with the theoretical method to a 6th degree polynomial expression. The obtained polynomial expression (which can be written in general form as shown in Eq. [D- 29]), can then be fed into the CFD solver as an UDE.

6 5 4 3 2 C p = aT + bT + cT + dT + eT + fT + g [D-29]

A sample of the obtained coefficients for Eq. [D-29] for estimating the heat capacity of supercritical CO2 can be seen in Table D-5, and a graphical representation of the previously stated polynomial fitting is shown in Figure D-10.

P [MPa] a b c d e f g

8 1.24 x 10-7 -2.89 x 10-4 2.80 x 10-1 -1.44 x 102 4.17 x 104 -6.41 x 106 4.10 x 108 10 -9.00 x 10-8 2.08 x 10-4 -2.01 x 10-1 1.03 x 102 -2.94 x 104 4.48 x 106 -2.83 x 108

12 -2.76 x 10-9 7.70 x 10-6 -8.72 x 10-3 5.16 x 100 -1.69 x 103 2.90 x 105 -2.05 x 107

14 1.49 x 10-8 -3.40 x 10-5 3.21 x 10-2 -1.61 x 101 4.52 x 103 -6.73 x 105 4.15 x 107

16 8.67 x 10-9 -2.01 x 10-5 1.93 x 10-2 -9.85 x 100 2.81 x 103 -4.27 x 105 2.68 x 107

18 2.86 x 10-9 -6.79 x 10-6 6.70 x 10-3 -3.50 x 100 1.02 x 103 -1.59 x 105 1.02 x 107

20 3.46 x 10-10 -9.59 x 10-7 1.08 x 10-3 -6.27 x 10-1 2.01 x 102 -3.37 x 104 2.32 x 106

22 -3.54 x 10-10 7.17 x 10-7 -5.88 x 10-4 2.48 x 10-1 -5.60 x 101 6.29 x 103 -2.59 x 105

Table D-5. Obtained coefficients for Eq. [D-29] for supercritical CO2

10000

7000 [J/kmol· K] [J/kmol·

p

C 4000

1000 310 345 380 415 450 Temperature [K] 8 MPa 10 MPa 12 MPa 14 MPa 16 MPa 18 MPa 20 MPa 22 MPa

Figure D-10. Heat capacity polynomial fitting for supercritical CO2

- 215 - Appendix D

In order to quantify the round-up error associated with the numerical solver, a simulation for a heat capacity-varying flow of supercritical CO2 in a Venturi tube was set, and obtained numerical values were compared with the prediction of the residual heat capacity method method, resulting in a reasonably good agreement between the numerical results and the theoretical estimation (see Figure D-11). The estimated error for the heat capacity was never superior to 15 %, which was considered to be within an acceptable range.

2100

from CFD 1900 [J/kg· K] [J/kg·

p C

1700 1700 1900 2100

C p [J/kg·K] from estimation

Figure D-11. Heat capacity parity plot for supercritical CO2 (numerical results vs. residual heat capacity method)

References

Fluent Inc., (2005). Fluent 6.2 user’s guide. Fluent Inc.

Guardo, A., Oliver, A., Larrayoz, M.A., (2005). Using CFD simulations to estimate properties of a supercritical binary mixture. Proceedings of the 10th meeting on supercritical fluids. Colmar, France. [CD-ROM]

Lucas, K., (1980). Phase equilibria and fluid properties in the chemical industry. Dechema, Frankfurt, p. 573.

Peng, D.Y. and Robinson, D.B., (1976). A new two constant equation of state. Industrial & Engineering Chemistry Fundamentals, 15, 59 – 64.

Redlich, O. and Kwong, J.N.S., (1949). On the thermodynamics of solutions V: an equation of state. Fugacities of gaseous solutions. Chemical Reviews, 44, 233 – 244.

Reid, R.C., Prausnitz, J.M., Poling, B.P., (1987). The properties of gases and liquids. Forth edition. McGraw-Hill, Boston.

Stiel, L.I. and Thodos, G., (1964). The thermal conductivity of nonpolar substances in the dense gaseous and liquid regions. AIChE Journal, 10, 26 – 30.

Yaws, C.L., (1999). Chemical properties handbook. McGraw-Hill, New York.

- 216 - APPENDIX E

BOUNDARY CONDITIONS Description & inputs for setting up a cfd simulation

James Stanley Daughtery, « Boundary »

Appendix E

Boundary conditions specify the flow, thermal and species composition variables on the boundaries of a physical model. They are, therefore, a critical component of CFD simulations and it is important that they are specified appropriately.

E-1. Used boundary types

The boundary types used in this work are classified as follows:

• Flow inlet and exit boundaries: velocity inlet, mass flow inlet, pressure outlet.

• Wall, repeating, and pole boundaries: wall, symmetry.

• Internal cell zones: fluid, solid (porous is a type of fluid zone)

In this appendix, the boundary conditions listed above will be described, and an explanation of how to set them and when they are most appropriately used will be provided.

E-2. Flow inlets and exits

Commercial CFD codes have a wide range of boundary conditions that permit flow to enter and exit the solution domain. This section includes descriptions of how each type of condition selected for this work is used, and what information is needed for each one. Recommendations for determining inlet values of the turbulence parameters are also provided.

E-2.1. Using flow boundary conditions

The inlet and exit boundary condition options used in this work are as follows:

• Velocity inlet boundary conditions are used to define the velocity and scalar properties of the flow at inlet boundaries.

• Mass flow inlet boundary conditions are used in compressible flows to prescribe a mass flow rate at an inlet. It is not necessary to use mass flow inlets in incompressible flows because when density is constant, velocity inlet boundary conditions will fix the mass flow.

• Pressure outlet boundary conditions are used to define the static pressure at flow outlets (and also other scalar variables, in case of back flow). The use of a pressure outlet boundary condition instead of an outflow condition often results in a better rate of convergence when backflow occurs during iteration.

• Outflow boundary conditions are used to model flow exits where the details of the flow velocity and pressure are not known prior to solution of the flow problem. They are appropriate where the exit flow is close to a fully developed condition, as the outflow boundary condition assumes a zero normal gradient for all flow variables except pressure. They are not appropriate for compressible flow calculations.

- 219 - Appendix E

E-2.2. Determining turbulence parameters

When the flow enters the domain at an inlet, outlet, or far-field boundary, the CFD code requires specification of transported turbulence quantities. This section describes which quantities are needed for specific turbulence models and how they must be specified. It also provides guidelines for the most appropriate way of determining the inflow boundary values.

E-2.2.1. Uniform specification of turbulence quantities

In some situations, as the ones presented in this work, it is appropriate to specify a uniform value of the turbulence quantity at the boundary where inflow occurs. Examples are fluid entering a duct, far-field boundaries, or even fully-developed duct flows where accurate profiles of turbulence quantities are unknown.

In most turbulent flows, higher levels of turbulence are generated within shear layers than enter the domain at flow boundaries, making the result of the calculation relatively insensitive to the inflow boundary values. Nevertheless, caution must be used to ensure that boundary values are not so unphysical as to contaminate your solution or impede convergence. This is particularly true of external flows where unphysically large values of effective viscosity in the free stream can “swamp” the boundary layers.

You can use turbulence specification methods to enter uniform constant values instead of profiles. Alternatively, you can specify the turbulence quantities in terms of more convenient quantities such as turbulence intensity, turbulent viscosity ratio, hydraulic diameter, and turbulence length scale. These quantities are discussed further in the following sections.

E-2.2.2. Turbulence intensity

The turbulence intensity, I , is defined as the ratio of the root-mean-square of the velocity fluctuations, u′ , to the mean flow velocity, u .

A turbulence intensity of 1% or less is generally considered low and turbulence intensities greater than 10% are considered high. Ideally, you will have a good estimate of the turbulence intensity at the inlet boundary from external, measured data. For example, if you are simulating a wind-tunnel experiment, the turbulence intensity in the free stream is usually available from the tunnel characteristics. In modern low-turbulence wind tunnels, the free-stream turbulence intensity may be as low as 0.05%.

For internal flows, the turbulence intensity at the inlets is totally dependent on the upstream history of the flow. If the flow upstream is under-developed and undisturbed, you can use a low turbulence intensity. If the flow is fully developed, the turbulence intensity may be as high as a few percent. The turbulence intensity at the core of a fully-developed duct flow can be estimated from the following formula derived from an empirical correlation for pipe flows:

u′ −1 I ≡ = 0.16()Re 8 [E-1] u DH

At a Reynolds number of 50,000, for example, the turbulence intensity will be 4%, according to this formula.

- 220 - Appendix E

E-2.2.3. Turbulence length scale and hydraulic diameter

The turbulence length scale, l , is a physical quantity related to the size of the large eddies that contain the energy in turbulent flows. In fully-developed duct flows, l is restricted by the size of the duct, since the turbulent eddies cannot be larger than the duct. An approximate relationship between l and the physical size of the duct is

l = 0.07L [E-2] where L is the relevant dimension of the duct. The factor of 0.07 is based on the maximum value of the mixing length in fully-developed turbulent pipe flow, where L is the diameter of the pipe. In a channel of non-circular cross-section, you can base L on the hydraulic diameter. If the turbulence derives its characteristic length from an obstacle in the flow, such as a perforated plate, it is more appropriate to base the turbulence length scale on the characteristic length of the obstacle rather than on the duct size. It should be noted that the relationship of Equation [E-2], which relates a physical dimension ( L ) to the turbulence length scale ( l ), is not necessarily applicable to all situations. For most cases, however, it is a suitable approximation. Guidelines for choosing the characteristic length L or the turbulence length scale l for selected flow types are listed below:

• For fully-developed internal flows, choose the Intensity and Hydraulic Diameter

specification method and specify the hydraulic diameter L = DH in the Hydraulic Diameter field.

• For flows downstream of turning vanes, perforated plates, etc., choose the Intensity and Hydraulic Diameter method and specify the characteristic length of the flow opening for L in the Hydraulic Diameter field.

• For wall-bounded flows in which the inlets involve a turbulent boundary layer, choose

the Intensity and Length Scale method and use the boundary-layer thickness, δ 99 , to

compute the turbulence length scale, l , from l = 0.4δ 99 . Enter this value for l in the Turbulence Length Scale field.

E-2.2.4. Turbulent viscosity ratio

The turbulent viscosity ratio, µt µ , is directly proportional to the turbulent Reynolds number 2 (Ret ≡ κ εν ). Ret is large (on the order of 100 to 1000) in high-Reynolds-number boundary layers, shear layers, and fully-developed duct flows. However, at the free-stream boundaries of most external flows, µt µ is fairly small. Typically, the turbulence parameters are set so that

1 < µt µ < 10 .

To specify quantities in terms of the turbulent viscosity ratio, you can choose Turbulent Viscosity Ratio (for the Spalart-Allmaras model) or Intensity and Viscosity Ratio (for the κ − ε models and the κ − ω models).

- 221 - Appendix E

E-2.3. Velocity inlet boundary conditions

Velocity inlet boundary conditions are used to define the flow velocity, along with all relevant scalar properties of the flow, at flow inlets. The total (or stagnation) properties of the flow are not fixed, so they will rise to whatever value is necessary to provide the prescribed velocity distribution.

This boundary condition is intended for incompressible flows, and its use in compressible flows will lead to a non-physical result because it allows stagnation conditions to float to any level. You should also be careful not to place a velocity inlet too close to a solid obstruction, since this could cause the inflow stagnation properties to become highly non-uniform.

In special instances, a velocity inlet may be used to define the flow velocity at flow exits. (The scalar inputs are not used in such cases.) In such cases you must ensure that overall continuity is maintained in the domain.

You will enter the following information for a velocity inlet boundary:

• Velocity magnitude and direction or velocity components

• Temperature (for energy calculations)

• Turbulence parameters (for turbulent calculations)

• Chemical species mass fractions (for species calculations)

When your velocity inlet boundary condition defines flow entering the physical domain of the model, the CFD code uses both the velocity components and the scalar quantities that you defined as boundary conditions to compute the inlet mass flow rate, momentum fluxes, and fluxes of energy and chemical species. The mass flow rate entering a fluid cell adjacent to a velocity inlet boundary is computed as:

m& = ∫ ρ ⋅ u ⋅ dA [E-3]

Note that only the velocity component normal to the control volume face contributes to the inlet mass flow rate.

Density at the inlet plane is either constant or calculated as a function of temperature, pressure, and/or species mass fractions, where the mass fractions are the values you entered as an inlet condition.

E-2.4. Mass flow inlet boundary conditions

Mass flow boundary conditions can be used to provide a prescribed mass flow rate or mass flux distribution at an inlet. Physically, specifying the mass flux permits the total pressure to vary in response to the interior solution.

A mass flow inlet is often used when it is more important to match a prescribed mass flow rate than to match the total pressure of the inflow stream. An example is the case of a small cooling jet that is bled into the main flow at a fixed mass flow rate, while the velocity of the main flow is governed primarily by a (different) pressure inlet/outlet boundary condition pair.

- 222 - Appendix E

The adjustment of inlet total pressure might result in a slower convergence, so if both the pressure inlet boundary condition and the mass flow inlet boundary condition are acceptable choices, you should choose the former.

It is not necessary to use mass flow inlets in incompressible flows because when density is constant, velocity inlet boundary conditions will fix the mass flow.

You will enter the following information for a mass flow inlet boundary:

• Mass flow rate, mass flux, or (primarily for the mixing plane model) mass flux with average mass flux

• Total (stagnation) temperature

• Static pressure

• Flow direction

• Turbulence parameters (for turbulent calculations)

• Chemical species mass fractions (for species calculations)

When mass flow boundary conditions are used for an inlet zone, a velocity is computed for each face in that zone, and this velocity is used to compute the fluxes of all relevant solution variables into the domain. With each iteration, the computed velocity is adjusted so that the correct mass flow value is maintained. To compute this velocity, your inputs for mass flow rate, flow direction, static pressure, and total temperature are used.

There are two ways to specify the mass flow rate. The first is to specify the total mass flow rate,

m& , for the inlet. The second is to specify the mass flux, ρ ⋅un (mass flow rate per unit area). If a total mass flow rate is specified, the CFD code converts it internally to a uniform mass flux by dividing the mass flow rate by the total inlet area:

m ρ ⋅u = & [E-4] n A

If the direct mass flux specification option is used, the mass flux can be varied over the boundary by using profile files or user-defined functions. If the average mass flux is also specified (either explicitly by you or automatically by the CFD code), it is used to correct the specified mass flux profile, as described earlier in this section. Once the value of ρ ⋅un at a given face has been determined, the density at the face must be determined in order to find the normal velocity. The manner in which the density is obtained depends upon whether the fluid is modeled as an ideal gas or not. Each of these cases is examined below.

If the fluid is an ideal gas, the static temperature and static pressure are required to compute the density:

p = ρ ⋅ R ⋅T [E-5]

If the inlet is supersonic, the static pressure used is the value that has been set as a boundary condition. If the inlet is subsonic, the static pressure is extrapolated from the cells inside the inlet face.

- 223 - Appendix E

The static temperature at the inlet is computed from the total enthalpy, which is determined from the total temperature that has been set as a boundary condition. The total enthalpy is given by

1 2 h ()T = h ()T + u [E-6] 0 0 2 where the velocity is related to the mass flow rate given by Equation [E-4]. Using Equation [E-5] to relate density to the (known) static pressure and (unknown) temperature, Equation [E-6] can be solved to obtain the static temperature.

When you are modeling incompressible flows, the static temperature is equal to the total temperature. The density at the inlet is either constant or readily computed as a function of the temperature and (optionally) the species mass fractions. The velocity is then computed using Equation [E-4].

To compute the fluxes of all variables at the inlet, the flux velocity is used along with the inlet value of the variable in question. For example, the flux of turbulence kinetic energy is ρ ⋅κ ⋅un . These fluxes are used as boundary conditions for the corresponding conservation equations during the course of the solution.

E-2.5. Pressure outlet boundary conditions

Pressure outlet boundary conditions require the specification of a static (gauge) pressure at the outlet boundary. The value of the specified static pressure is used only while the flow is subsonic. Should the flow become locally supersonic, the specified pressure will no longer be used; pressure will be extrapolated from the flow in the interior. All other flow quantities are extrapolated from the interior.

A set of "backflow'' conditions is also specified should the flow reverse direction at the pressure outlet boundary during the solution process. Convergence difficulties will be minimized if you specify realistic values for the backflow quantities.

You will enter the following information for a pressure outlet boundary:

• Static pressure

• Backflow conditions

o Total (stagnation) temperature (for energy calculations)

o Backflow direction specification method

o Turbulence parameters (for turbulent calculations)

o Chemical species mass fractions (for species calculations)

• Target mass flow rate (not available for multiphase flows)

- 224 - Appendix E

At pressure outlets, the CFD code uses the boundary condition pressure you input as the static pressure of the fluid at the outlet plane, and extrapolates all other conditions from the interior of the domain.

E-2.6. Outflow boundary conditions

Outflow boundary conditions are used to model flow exits where the details of the flow velocity and pressure are not known prior to solution of the flow problem. You do not define any conditions at outflow boundaries (unless you are modeling radiative heat transfer, a discrete phase of particles, or split mass flow): the CFD code extrapolates the required information from the interior. It is important, however, to understand the limitations of this boundary type.

Note that outflow boundaries cannot be used in the following cases:

• if a problem includes pressure inlet boundaries; use pressure outlet boundary conditions instead

• if you are modeling compressible flow

• if you are modeling unsteady flows with varying density, even if the flow is incompressible

The boundary conditions used at outflow boundaries are as follows:

• A zero diffusion flux for all flow variables

• An overall mass balance correction

The zero diffusion flux condition applied at outflow cells means that the conditions of the outflow plane are extrapolated from within the domain and have no impact on the upstream flow. The extrapolation procedure used updates the outflow velocity and pressure in a manner that is consistent with a fully-developed flow assumption, as noted below, when there is no area change at the outflow boundary.

The zero diffusion flux condition applied at outflow boundaries is approached physically in fully-developed flows. Fully-developed flows are flows in which the flow velocity profile (and/or profiles of other properties such as temperature) is unchanging in the flow direction.

It is important to note that gradients in the cross-stream direction may exist at an outflow boundary. Only the diffusion fluxes in the direction normal to the exit plane are assumed to be zero.

E-3. Wall boundary conditions

Wall boundary conditions are used to bound fluid and solid regions. In viscous flows, the no- slip boundary condition is enforced at walls by default, but you can specify a tangential velocity component in terms of the translational or rotational motion of the wall boundary, or model a "slip'' wall by specifying shear. (You can also model a slip wall with zero shear using the symmetry boundary type, but using a symmetry boundary will apply symmetry conditions for

- 225 - Appendix E

all equations). The shear stress and heat transfer between the fluid and wall are computed based on the flow details in the local flow field.

You will enter the following information for a wall boundary:

• Thermal boundary conditions (for heat transfer calculations)

• Wall motion conditions (for moving or rotating walls)

• Shear conditions (for slip walls, optional)

• Wall roughness (for turbulent flows, optional)

• Species boundary conditions (for species calculations)

When you are solving the energy equation, you need to define thermal boundary conditions at wall boundaries. Five types of thermal conditions are available:

• Fixed heat flux

• Fixed temperature

• Convective heat transfer

• External radiation heat transfer

• Combined external radiation and convection heat transfer

For no-slip wall conditions, the CFD code uses the properties of the flow adjacent to the wall/fluid boundary to predict the shear stress on the fluid at the wall. In laminar flows this calculation simply depends on the velocity gradient at the wall, while in turbulent flows one of the approaches described in Appendix B is used. For specified-shear walls, it will compute the tangential velocity at the boundary. If you are modeling inviscid flow, all walls use a slip condition, so they are frictionless and exert no shear stress on the adjacent fluid.

In a laminar flow , the wall shear stress is defined by the normal velocity gradient at the wall as

∂u τ = µ [E-7] w ∂n

When there is a steep velocity gradient at the wall, you must be sure that the grid is sufficiently fine to accurately resolve the boundary layer. Wall treatments for turbulent flows are described in Appendix B.

When a fixed temperature condition is applied at the wall, the heat flux to the wall from a fluid cell is computed as

q = h f (Tw − T f )+ qrad [E-8]

Note that the fluid-side heat transfer coefficient is computed based on the local flow-field conditions (e.g., turbulence level, temperature, and velocity profiles).

- 226 - Appendix E

When you define a heat flux boundary condition at a wall, you specify the heat flux at the wall surface. The CFD code uses Equation [E-8] and your input of heat flux to determine the wall surface temperature adjacent to a fluid cell as

q − qrad Tw = + T f [E-9] h f where, as noted above, the fluid-side heat transfer coefficient is computed based on the local flow-field conditions.

In laminar flows, the fluid side heat transfer at walls is computed using Fourier's law applied at the walls in its discrete form:

⎛ ∂T ⎞ q = k f ⎜ ⎟ [E-10] ⎝ ∂n ⎠ w where n is the local coordinate normal to the wall.

For turbulent flows, the CFD code uses the law-of-the-wall for temperature derived using the analogy between heat and momentum transfer. See Appendix B for details.

E-4. Symmetry boundary conditions

Symmetry boundary conditions are used when the physical geometry of interest, and the expected pattern of the flow/thermal solution, have mirror symmetry. They can also be used to model zero-shear slip walls in viscous flows. This section describes the treatment of the flow at symmetry planes and provides examples of the use of symmetry. You do not define any boundary conditions at symmetry boundaries, but you must take care to correctly define your symmetry boundary locations.

Symmetry boundaries are used to reduce the extent of your computational model to a symmetric subsection of the overall physical system. Figures E-1 and E-2 illustrate two examples of symmetry boundary conditions used in this way.

Figure E-1. Use of symmetry to model one quarter of a 3D duct

- 227 - Appendix E

Figure E-2. Use of symmetry to model one quarter of a circular cross-section

The CFD code assumes a zero flux of all quantities across a symmetry boundary. There is no convective flux across a symmetry plane: the normal velocity component at the symmetry plane is thus zero. There is no diffusion flux across a symmetry plane: the normal gradients of all flow variables are thus zero at the symmetry plane. The symmetry boundary condition can therefore be summarized as follows:

• Zero normal velocity at a symmetry plane

• Zero normal gradients of all variables at a symmetry plane

As stated above, these conditions determine a zero flux across the symmetry plane, which is required by the definition of symmetry. Since the shear stress is zero at a symmetry boundary, it can also be interpreted as a "slip'' wall when used in viscous flow calculations.

E-5. Fluid conditions

A fluid zone is a group of cells for which all active equations are solved. The only required input for a fluid zone is the type of fluid material. You must indicate which material the fluid zone contains so that the appropriate material properties will be used.

Optional inputs allow you to set sources or fixed values of mass, momentum, heat (temperature), turbulence, species, and other scalar quantities. You can also define motion for the fluid zone. If there are rotationally periodic boundaries adjacent to the fluid zone, you will need to specify the rotation axis. If you are modeling turbulence using one of the κ − ε models, the κ − ω model, or the Spalart-Allmaras model, you can choose to define the fluid zone as a laminar flow region.

- 228 - APPENDIX F

LIST OF PUBLICATIONS in journals & congresses

´

Acclaimed Images, « List »

Appendix F

F-1. Articles in journals

Industrial & Engineering Chemistry Research; 2004, 43, 7049 – 7056 CFD flow and heat transfer in nonregular packings for fixed bed equipment design A. Guardo, M. Coussirat, M.A. Larrayoz, F. Recasens, E. Egusquiza.

Abstract This work aims to test the application of computational fluid dynamics (CFD) modeling to fixed bed equipment design. Studies of CFD with a fixed bed design commonly use a regular packing approach to define bed geometry. However, assuming nonregular packing is a more realistic way to simulate the behavior of a fixed bed and therefore to estimate important design parameters. As a fluid flow simulation tool, CFD allows us to obtain a more accurate view of the fluid flow and heat transfer mechanisms present in fixed bed equipment. Forty-four spheres stacked in a nonregular maximum-space-occupying arrangement in a cylindrical container were used as the geometrical model. Estimates of the pressure drop along the bed, and wall heat transfer parameters were chosen as validation parameters. ¢P, Nuw, and kr/kf are given for different values of Re (transition and turbulent flow), and they are compared to commonly used correlations. Air was chosen as the flowing fluid. Cases of laminar and turbulent flows are presented, and their results are compared. To account for the fluid flow and thermally fluctuating components in the turbulent cases, one- and two- equation turbulence models were used for simulation.

Chemical Engineering Science; 2005, 60, 1733 – 1742 Influence of the turbulence model in CFD modeling of wall-to-fluid heat transfer in packed beds A. Guardo, M. Coussirat, M.A. Larrayoz, F. Recasens, E. Egusquiza.

Abstract Computational fluid dynamics as a simulation tool allows obtaining a more detailed view of the fluid flow and heat transfer mechanisms in fixed-bed reactors, through the resolution of 3D Reynolds averaged transport equations, together with a turbulence model when needed. In this way, this tool permits obtaining of mean and fluctuating flow and temperature values in any point of the bed. An important problem when modeling a turbulent flow fixed-bed reactor is to decide which turbulence model is the most accurate for this situation. To gain insight into this subject, this study presents a comparison between the performance in flow and heat transfer estimation of five different RANS turbulence models in a fixed bed composed of 44 homogeneous stacked spheres in a maximum space-occupying arrangement in a cylindrical container by solving the 3D Navier–Stokes and energy equations by means of a commercial finite volume code, Fluent 6.0 ®. Air is chosen as flowing fluid. Numerical pressure drop, velocity and thermal fields within the bed are obtained. In order to judge the capabilities of these turbulence models, heat transfer parameters (Nuw, kr/kf) are estimated from numerical data and together with the pressure drop are compared to commonly used correlations for parameter estimations in fixed-bed reactors.

- 231 - Appendix F

Chemical Engineering Science; 2006, 61, 4341 – 4353 CFD study on particle-to-fluid heat transfer in fixed bed reactors: Convective heat transfer at low and high pressure A. Guardo, M. Coussirat, F. Recasens, M.A. Larrayoz, X. Escaler.

Abstract Computational fluid dynamics (CFD) has proven to be a reliable tool for fixed bed reactor design, through the resolution of 3D transport equations for mass, momentum and energy balances. Solution of these equations allows to obtain velocity and temperature profiles within the reactor. The numerical results obtained allow estimating useful parameters applicable to equipment design. Particle-to-fluid heat transfer coefficient is of primal importance when analyzing the performance of a fixed bed reactor. To gain insight in this subject, numerical results using a modified commercial CFD solver are presented and particle-to-fluid heat transfer in fixed beds is analyzed. Two different configurations are studied: forced convection at low pressure (with air as circulating fluid) and mixed (i.e., free + forced) convection at high pressure (with supercritical CO2 as circulating fluid). In order to impose supercritical fluid properties to the model, modifications into the CFD code were introduced by means of user defined functions (UDF) and user defined equations (UDE). The obtained numerical data is compared to previously published data and a novel CFD-based correlation (for free, forced and mixed convection at high pressure) is presented.

Chemical Engineering Science; 2006 (Submitted – under review) CFD studies on particle-to-fluid mass and heat transfer in packed beds: free convection effects in supercritical fluids A. Guardo, M. Coussirat, F. Recasens, M.A. Larrayoz, X. Escaler.

Abstract Free convection effects of heat and mass transfer are of prime importance when modeling the behavior of supercritical fluids in packed bed reaction equipment and in solid extractors. Density gradients, caused by temperature or composition variations, tend to control the overall flow pattern of the fluid, making the buoyancy terms of the Navier-Stokes equations an important term to model when doing a Computational Fluid Dynamics (CFD) simulation under the described conditions. The purpose of this work was to visualize by means of CFD techniques the influence of the buoyancy forces over flow patterns and the convective flow in a packed bed filled with a supercritical fluid, because the experimental option is very expensive and time demanding. Computations were made using a 3-dimensional CFD modeling strategy. Carbon dioxide in the supercritical conditions was selected as a flowing fluid. Its transport properties at high pressure were incorporated within a CFD commercial code in order to estimate them online within the simulation process. Particle-to-fluid mass transfer in supercritical conditions was analyzed, and transport coefficients were obtained and validated. Recently, Guardo et al. (2006) have presented a correlation for particle-to-fluid heat transfer in supercritical conditions, based on an analogy with the correlation proposed by Stüber et al. (1996) for mass transfer. The obtained numerical results presented in this work validates the idea that the modified correlation presented by Guardo et al. (2006) can be used to describe heat transfer phenomena in a packed bed under mixed convection regime at high pressures.

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Chemical Engineering Science; 2006 (Submitted – under review) CFD modeling on external mass transfer and intra-particle diffusional effects on the supercritical hydrogenation of sunflower oil A. Guardo, M. Casanovas, I. Magaña, D. Martínez, E. Ramírez, M.A. Larrayoz, F. Recasens.

Abstract Computational Fluid Dynamics (CFD) is applied to the study of the catalytic hydrogenation of sunflower oil in the presence of a supercritical solvent. A 2D CFD model of a single Pd-based catalyst pellet is presented. Intra-particle concentration profiles for all species present in the mixture (oil triglycerides and hydrogen) are obtained and compared against experimental results. Different particle sizes are studied and external mass transfer and intra-particle diffusional effects are analyzed. External mass transfer coefficients are obtained and presented. Fitting of the intra- particle concentration profiles allowed to verify the kinetic reaction model. Verified kinetic constants and intra-particle diffusion coefficients are fed into a 3D packed bed reactor model, and conversion profiles are obtained

F-2. Participation in congresses

9 Congreso Mediterráneo de Ingeniería Química CFD Approach to laminar-to-turbulent flow transition in fixed-bed equipment A. Guardo, M.A. Larrayoz, E. Velo, F. Recasens Barcelona, Spain. November 2002

Abstract In a bed of solid particles, at low flow rates the pressure drop has a linear dependency with the fluid velocity, while at high flow rates it varies approximately with the square of the fluid velocity. This is analogous to the law governing pressure drop in a pipe and has been corroborated with experimental data in some studies (Jolls et al., 1966). However, the existing relationship between fluid velocity and pressure drop, heat and mass transfer parameters in packed beds do not show sharp changes of the type noted at the transition to turbulence in pipes.

Computational Fluid Dynamics (CFD) has proven to be a powerful tool to numerically solve Navier-Stokes equations. There has been a fast growing in the study of applications in fluid flow and heat transfer, and several authors have used it to analyse flow patterns in fixed-bed equipment and to predict heat transfer parameters in studied cases (Dixon et al., 2001. Logtenberg et al., 1999).

However, laminar-to-turbulent flow transition has not been extensively reviewed with CFD numerical simulation, and there is still doubts about when the turbulent model should be activated, because there is no reliable guideline to predict the flow transition in complex geometries, like fixed bed reactors or extraction equipment.

In this work, the Navier-Stokes equations for steady-state fluid flow through a fixed bed are solved using a commercially available finite element code, GiD® (Rienzi and

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Font, 2001). This software includes modules for geometrical model generation, mesh generation, solution of equations and graphical post-processing of numerical results.

Fluid analyzed is taken to be CO2 in supercritial conditions (165 bar, 310 K) and in steady state flow regime, with an uniform velocity at the bed inlet and a fixed velocity condition around the spheres and at the wall. No thermal boundary conditions are applied to the model. Several numerical simulations are developed at different values of Re. Results are presented based on numerical convergency of the method.

6th International Symposium on Supercritical Fluids. Regular packing types for CFD simulation of SCF extraction and reaction equipment A. Guardo, M.A. Larrayoz, F. Recasens Versailles, France. June 2003

Abstract Fixed-bed structure study and its effects in heat and mass transfer have been widely reviewed by several authors. Determination of geometrical properties of randomly packed beds with computational simulation has conduced to the development of algorithms for simulating the construction of random packings in cylindrical containers [1]. Regular packing of spheres has also been studied, developing uniform geometries [2-4]; geometry-based models and computational algorithms have permitted to develop flow profile predictions in packed columns [5].

Computational Fluid Dynamics (CFD) has proven to be a powerful tool to numerically solve Navier-Stokes equations. There has been a fast growing in the study of applications in fluid flow and heat transfer, and several authors have used it to analyse flow patterns in fixed-bed equipment and to predict heat transfer parameters in studied cases [6-7]

In this work we review prior approaches to geometrical sphere-based models for packed beds and propose three different geometrical arranges for packed bed equipment computer simulation.

These models are used in CFD simulation of supercritical extraction and reaction equipment. Fluid analyzed is taken to be CO2 in supercritical conditions (165 bar, 310 K), with a uniform velocity at the bed inlet and a fixed velocity condition around the spheres and at the wall. No thermal boundary conditions are applied to the model.

18th International Symposium on Chemical Reaction Engineering. Influence of the Turbulence Model in CFD Modeling of Wall-to-Fluid Heat Transfer in Packed Beds A. Guardo, M. Coussirat, M.A. Larrayoz, F. Recasens, E. Egusquiza. Chemical Engineering Science; 2005, 60, 1733 – 1742 Abstract in prior section. Chicago, USA. June 2004

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7th Italian Conference on Supercritical Fluids and their applications / 9th European Meeting on Supercritical Fluids CFD simulation of particle-to-fluid heat transfer under supercritical conditions: preliminary results A. Guardo, F. Recasens, M.A. Larrayoz, M. Fernández Trieste, Italy. June 2004

Abstract Computational Fluid Dynamics (CFD) has proven to be a powerful tool to numerically solve the fluid-flow equations so it gives a further insight into the flow pattern of contacting equipment. There has been a fast growing in the study of applications in fluid flow and heat transfer, and several authors have used it to analyze flow patterns in fixed-bed equipment and to predict heat transfer parameters in studied cases [1-2].

In this work, particle-to-fluid heat transfer is studied in a maximum-occupying-space arrangement of solid spheres in a cylindrical container, in order to simulate via CFD the heat transfer behavior in a supercritical catalytic reactor under steady state conditions.

Supercritical CO2 was chosen as circulating fluid; a physical properties database (i.e., density, enthalpy, entropy, Cp, Cv, viscosity, thermal conductivity and sound speed) was created using accepted correlations for high pressure properties estimation [3-4]. Properties database was fed to CFD simulator in order to do properties estimation simultaneously with the mass, momentum and energy calculations. Buoyancy terms were found to be relevant in calculations through a dimensionless analysis done to Navier-Stokes equations, and therefore are used during simulations. C-written programme modules based on the SRK equation of state[4], have been implemented for use with CFD commercial codes. For transport properties, the available correlations have been implemented in the modules.

Navier-Stokes equations together with energy balance are solved using a commercially available finite volume-element code (Ansys CFX 5.6 solver). Fluid is taken to be compressible, Newtonian and in laminar or transitional flow regime (10 < Re < 300). So this gives a wider application to SCF cases.

Boundary conditions applied to the model are as follows:

• Constant velocity and temperature of the fluid at the inlet • Constant temperature of the particles surface • Constant pressure defined within a point of the fluid • Adiabatic reactor’s wall • Non-slip condition applied to wall and particle surfaces

Velocity and temperature fields are obtained within the bed, together with physical properties profiles at any point of the fluid domain. Energy balances permit to calculate heat transfer parameters (i.e., hp, Nup, jh) at different operating conditions, and estimated values via CFD are compared with previously published heat transfer correlations estimated under similar conditions applied to simulations [5-6]

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7th International Symposium on Supercritical Fluids. Flow and Heat Transfer simulation in a packed bed with supercritical solvent: CFD modeling of free convection effects. A. Guardo, M.A. Larrayoz, F. Recasens Orlando, USA. May 2005

Abstract The CFD simulation methodology is applied to the study of the natural convection flow in equipment used in SCF technology that involves a packed bed of particles. The ranges of heat and mass transfer are defined where those free convective effects show themselves. This happens at low particle Reynolds number and large Rayleigh (either Gr·Pr or Gr·Sc) numbers, for both heat and mass transport. Rayleigh number is large in SCF because of liquid-like densities and gas-like viscosities.

Free convection effects of heat and mass transfer are of prime importance when modelling the behavior of supercritical fluids in empty tube and fixed bed reaction equipment and in solid extractors. Density gradients, caused by temperature or composition variations, tend to control the volume forces and the overall flow pattern of the fluid, making the buoyancy terms of the Navier-Stokes equations an important term to model when doing a CFD simulation of a fixed bed under the described conditions. Buoyancy and sinking forces interact with forced flow in extraction with SC fluids, making them sensitive to the gravity field.

The purpose of this work was to visualize the influence of the buoyancy forces over flow patterns and the convective flow in a fixed bed reactor using a 3-dimensional CFD modelling strategy. Carbon dioxide in the supercritical conditions has been selected as a flowing fluid. Their transport and thermal properties at high pressure are incorporated within the CFD code in order to estimate them online within the simulation process. Particle-to-fluid heat transfer is analysed, and heat transfer coefficients are determined and compared against empirical correlations available in the literature. Flow and temperature fields are presented, and flow pattern is analysed according to density profiles obtained. Upflow and downflow operation regimes are modelled in order to study the influence of gravity on the free convection effects and on the heat transfer rate in the reactor. Different inflow velocities have been selected (5 < Re < 100) and contribution of free and forced convection effects is analyzed in each case.

2ª Reunión de expertos en fluidos comprimidos Hidrogenación catalítica de aceite de girasol en fluido supercrítico y su interés en la industria alimentaria A. Guardo, M.A. Larrayoz, E. Ramírez, F. Recasens, A. Santana No abstract available Valladolid, Spain. October 2005

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10th European Meeting on Supercritical Fluids Using CFD simulations to estimate properties of a supercritical binary mixture. A. Guardo, A. Oliver, M.A. Larrayoz Colmar, France. December 2005.

Abstract Computational Fluid Dynamics (CFD) has proven to be a powerful tool to numerically solve the fluid-flow equations so it gives a further insight into the flow pattern of contacting equipment. There has been a fast growing in the study of applications in fluid flow and heat and mass transfer. [1-2].

In this work, a prediction of the physical properties of a near-critical or supercritical binary mixture is obtained via CFD simulations in a 2D pipe flow model. Near-critical or supercritical CO2 was chosen as circulating fluid; in order to obtain a composition profile, a mass flux of toluene was imposed through the pipe walls. In order to impose near-critical or supercritical conditions to the fluid, Peng Robinson equation of state was imposed to the CFD model through a C++ subroutine compiled within the solver CFD code.

Navier-Stokes equations together with Peng Robinson EOS and species transport and diffusion model are solved using a commercially available finite volume-element code (Fluent 6.2 solver). Fluid is taken to be compressible, Newtonian and in laminar flow regime.

10th European Meeting on Supercritical Fluids CFD flow and mass transfer simulation in a packed bed with supercritical solvent: preliminary results A. Guardo, M. Coussirat, M.A. Larrayoz, F. Recasens Colmar, France. December 2005.

Abstract The CFD simulation methodology is applied to the study of the natural convection flow in equipment used in SCF technology that involves a packed bed of particles. The ranges of heat and mass transfer are defined where those free convective effects show themselves. This happens at low particle Reynolds number and large Rayleigh (either GrPr or GrSc) numbers, for both heat and mass transport. Rayleigh number is large in SCF because of liquid-like densities and gas-like viscosities.

Free convection effects of heat and mass transfer are of prime importance when modelling the behaviour of supercritical fluids in fixed bed reaction equipment and in solid extractors. Density gradients, caused by temperature or composition variations, tend to control the volume forces and the overall flow pattern of the fluid, making the buoyancy terms of the Navier – Stokes equations an important term to model when doing a CFD simulation of a fixed bed under the described conditions. Buoyancy and sinking forces interact with forced flow in extraction with SC fluids, making them sensitive to the gravity field [1-3]

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In order to simulate a supercritical extraction, a Toluene – CO2 system is analysed. Mesh sensitivity analysis is done and results of a preliminary simulation are shown. Carbon dioxide in the supercritical conditions has been selected as a flowing fluid. Their transport properties at high pressure are incorporated within a CFD commercial code in order to estimate them online within the simulation process. Particle-to-fluid mass transfer is analysed [4], and flow and composition contour fields are presented, and flow pattern is analysed according to density profiles obtained. Downflow operation regime is modelled.

19th International Symposium on Chemical Reaction Engineering. CFD studies on particle-to-fluid mass and heat transfer in packed beds: free convection effects in supercritical fluids A. Guardo, M. Coussirat, F. Recasens, M.A. Larrayoz, X. Escaler. Chemical Engineering Science; 2006 (Submitted – under review) Abstract in prior section. Potsdam. Germany. September 2006

19th International Symposium on Chemical Reaction Engineering. CFD modeling on external mass transfer and intra-particle diffusional effects on the supercritical hydrogenation of sunflower oil A. Guardo, M. Casanovas, I. Magaña, D. Martínez, E. Ramírez, M.A. Larrayoz, F. Recasens. Chemical Engineering Science; 2006 (Submitted – under review) Abstract in prior section. Potsdam, Germany. September 2006

5th International Symposium on High Pressure Processes Technology and Chemical Engineering Using CFD to estimate external mass transfer coefficients and intra-particle diffusional effects on the supercritical hydrogenation of sunflower oil A. Guardo, M. Casanovas, E. Ramírez, M.A. Larrayoz, F. Recasens. Segovia, Spain. June 2007 (Submitted)

Abstract Hydrogenation of vegetable oil is an important process in the food industry because of its widespread application to produce margarines, shortenings, and other food components. Supercritical technology has proven to be a reliable alternative to conventional hydrogenation process because not only the trans isomer levels can be reduced, but also offers a clean, economic and environmental friendly process. Computational Fluid Dynamics (CFD) modeling applied to the supercritical hydrogenation reaction can be useful in visualizing and understanding the mass transfer phenomena involved.

CFD is applied to the study of the catalytic hydrogenation of sunflower oil in the presence of a supercritical solvent. Intra-particle concentration profiles are obtained

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and compared against experimental data. Different temperatures, flow velocities and particle sizes are studied and external and internal mass transfer phenomena is analyzed.

The purpose of this work was to visualize the mass transfer phenomena over a single catalyst pellet and the intra-particle diffusional effects associated with particle size for the supercritical hydrogenation of sunflower oil using a 2D CFD modeling strategy. A mix of sunflower oil, hydrogen and supercritical propane (used as a solvent) is the flowing fluid. Their transport properties at high pressure are incorporated within a CFD commercial code in order to estimate them online within the simulation process.

A 2D CFD model of a single Pd-based catalyst pellet is presented. Intra-particle and surface concentration profiles and surface mass fluxes for all species present in the mixture (oil triglycerides and hydrogen) are obtained and compared against experimental results. Different temperatures, flow velocities and particle sizes are studied and external and internal mass transfer phenomena is analyzed. External mass transfer coefficients for hydrogen and oil triglycerides are obtained and a correlation for estimating them is presented.

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