Study of Turbulent Reacting Flow in a Jet Engine Combustor Using Large-Eddy Simulation
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Sixth International Symposium on Turbulence and Shear Flow Phenomena Seoul, Korea, 22-24 June 2009 STUDY OF TURBULENT REACTING FLOW IN A JET ENGINE COMBUSTOR USING LARGE-EDDY SIMULATION Donghyun You Department of Mechanical Engineering, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, USA [email protected] Frank Ham and Parviz Moin Center for Turbulence Research, Stanford University 488 Escondido Mall, Stanford, California 94305, USA [email protected], [email protected] ABSTRACT computationally efficient, RANS-based approaches are in Large-eddy simulation (LES) is carried out to under- general not capable enough to predict unsteady large-scale stand turbulent mixing, cooling, and combustion dynamics turbulent fluid motions which dominate mixing, chemical in a gas turbine engine combustor. An LES technique con- reaction rate, and cooling. LES is considered attractive serving discrete kinetic energy on arbitrary shaped unstruc- in predicting and understanding important flow features in tured grids is coupled with a Lagrangian particle tracking combustors, since it resolves large-scale energetic turbulent method for liquid fuel atomization and evaporation, and a motions which are mainly associated with the large-scale presumed probability density function approach for turbu- mixing and cooling. Kim & Syed (2004) and Mongia (2003) lent combustion. A systematic analysis of the mean and provide overviews on the importance and role of LES in de- turbulent flow fields elucidates dynamics of important flow signing advanced gas-turbine combustors. structures and mechanisms for turbulent mixing and cool- The present paper focuses on understanding complex ing. Predicted flow splits through the injector swirler and flow phenomena in a combustor realistic in terms of geome- inner and outer dilution shrouds are in good agreement with try and operation conditions. The accuracy and stability of experimental measurements. The mean temperature, tem- numerical algorithms used for the present LES is achieved by perature profile, and NO mole fraction at the exit of the conserving discrete kinetic energy as well as mass and mo- combustor are also found to agree well with experimental mentum. Geometric complexity of practical combustors is data. The dilution jets produce significant magnitudes of the overcome by the use of unstructured grids. Systematic val- derivatives of the mean radial and circumferential velocity idations of the present LES technique have been performed components, thereby dominating turbulent kinetic energy in the simulations of jets in cross flow, a model combustor production. The present study suggests that combustor exit with fuel spray, and flow through a rig-configuration of the flow is highly unsteady and consists of small-scale velocity Pratt & Whitney 6000 engine combustor. Some of these fluctuations and intermittent large-scale high temperature results have been reported in Moin & Apte (2007). flow structures. In the following sections, the computational methodology for simulating gas and liquid phase flows and results from LES of the Pratt & Whitney 6000 engine combustor are INTRODUCTION discussed in detail. The jet engine or gas-turbine engine combustors in- volve complex physical phenomena such as fuel spray break- COMPUTATIONAL METHODOLOGY up/evaporation/condensation, flow swirling, flow separa- tion, turbulent mixing of main stream and dilution cooling air, and hot combustion products including pollutants. The Numerical Methods for Gas Phase mixing and cooling performance of a combustor dilution sys- The numerical algorithm and solution methods are de- tem can be implicated in thermal failure of the downstream scribed in detail in Ham et al. (2006) and Moin & Apte turbine. Accurate observations and quantitative measure- (2007). The main features of the methodologies are sum- ments of these processes in real industrial configurations are marized here. The spatially filtered governing equations for difficult and expensive. Better understanding of these flows gas-phase are for design modifications, improvements, and exploring fun- damental physics demands high-fidelity numerical studies in (ρ ) +(ρ u ) = S˙ , (1) real industrial configurations. g ,t g j ,j m To date the engineering prediction of combustor flows in ρ u ρ u u μS − τ − p S˙ , realistic configurations has relied on the Reynolds-averaged ( g i),t +( g j i),j =(2 ij ),j ( ij ),j ( ),i + i (2) Navier-Stokes (RANS) equations (Malecki et al. 2001; ρ Z ρ u Z ρ α Z − q S˙ , Brankovic et al. 2001). In RANS, turbulence models for ( g ),t +( g j ),j =( g Z ( ),j ),j ( Z j ),j + Z (3) the Reynolds stress tensor provide time (or ensemble) av- ρ C ρ u C ρ α C − q ω , eraged solution to the Navier-Stokes equations. Although ( g ),t +( g j ),j =( g C ( ),j ),j ( C j ),j + ˙ C (4) 673 Sixth International Symposium on Turbulence and Shear Flow Phenomena Seoul, Korea, 22-24 June 2009 where the filtering operation is denoted by an overbar and the consideration of radiative heat losses and the additional Favre (density-weighted) filtering by a tilde. ρg, ui, p, μ, solution of a transport equation for the NO mass fraction. and Sij are the density, velocity, pressure, dynamic viscos- In (2)-(4), the eddy viscosity and eddy diffusivities are ity, and strain-rate tensor of gas-phase flow, respectively. Z evaluated using the dynamic Smagorinsky model of Moin et and C are the mixture fraction and progress variable, re- al. (1991) while the subfilter variance of the mixture frac- spectively, while αZ and αC are molecular diffusivities of tion is evaluated by the transported-filtered density function the corresponding scalars.ω ˙ C is the source term due to approach of Raman et al. (2005). chemical reactions. The additional terms in the continuity, S˙ , mixture fraction, S˙ , and momentum equations, S˙ , m Z i Numerical Methods for Liquid Phase are the interphase mass and momentum transport terms, The liquid-phase fuel spray is simulated in a Lagrangian which are obtained from the governing equations for spray framework with an efficient particle tracking scheme on droplet dynamics. The unclosed transport terms in the mo- unstructured grids, which allows simulation of millions of mentum and scalar equations are grouped into the residual independent droplet trajectories. The droplet motion is stress τ and residual scalar fluxes q and q . The choice ij Z j C j simulated using a version of the Basset-Boussinesq-Oseen of the progress variable C depends on the flow conditions equations: and chemistry. Typically, the mass fraction of major prod- uct species is a good indicator of the forward progress of the du ρ p D − − g , reaction. = pdrop(ug up)+ 1 g dt ρp The Cartesian velocity components and pressure are dx stored at the nodes of the computational elements. A nu- p = u , (7) dt p merical method that emphasizes discrete energy conserva- tion was developed for the above equations on unstructured where grids with hybrid, arbitrary elements (Ham et al., 2006). 3 ρ |u − u | Controlling aliasing errors using kinetic energy conservation D = C g g p , psolid d ρ d instead of employing numerical dissipation or filtering has 4 p p 24 been shown to provide good predictive capability for suc- C . Re0.687 , d = (1+015 p ) (8) cessful LES. Re All terms in (1)-(4) are advanced using a second-order ac- and the Basset force and added mass terms are neglected by curate fully-implicit method in time, and are discretized by the facts that the density ratio of the droplet to gas-phase the second-order central difference in space. A bi-conjugate fluid is about 103, droplet sizes are smaller than the turbu- gradient stabilized method (BCGSTAB) is used to solve the lence integral length scale, and that the effect of shear on discretized momentum equations. The Poisson-type equa- droplet motion is negligible. In (7) and (8), xp is the posi- tion is solved by an algebraic multigrid method. tion vector of the droplet centroid, up is the droplet velocity, The combustion chemistry is incorporated in the form ug is the gas-phase velocity interpolated to the droplet lo- of a steady state one-dimensional flamelet model as in cation, ρp and ρg are the droplet and gas-phase densities, the flamelet/progresive-variable approach of Pierce & Moin and g is the gravitational acceleration. The expression for (2004). The subgrid fluctuations in the mixture fraction, solid-body drag in (8) is modified to account for droplet de- progress variable, and filtered combustion variables are ob- formation and internal circulation (see Moin & Apte (2006) tained by integrating chemical state relationships over the for more details). Z C joint probability density function (PDF) of and .For The injected liquid jet/sheet is approximated by large example, the filtered chemical source term of the progress drops with size equal to the initial annular film thickness. variable is given as Then, a stochastic spray breakup model (Apte et al. 2003) generates a broad range of droplet sizes depending on Weber numbers. In this model, the characteristic radius of droplets ω˙ C = ω˙ C (Z, C)P (Z, C)dZdC. (5) is assumed to be a time-dependent stochastic variable with a given initial size distribution. The breakup of parent drops The joint subgrid PDF is modeled by writing P(Z, C)= into secondary droplets is viewed as the temporal and spatial P(C|Z)P(Z), where P(Z) is modeled by the presumed beta evolution of this distribution function around the parent- subgrid PDF and the conditional PDF P(C|Z) is modeled droplet size according to the Fokker-Planck (FP) differential as a delta function. The governing equation for the mixture equation (see Apte et al., 2003). As new droplets are formed, fraction in (3) consists of a source term due to the evapora- parent droplets are destroyed and Lagrangian tracking in the tion of liquid fuel spray.