An Overview of Turbulence Modeling

An overview of turbulence modeling

P.A. Durbin1 & T.I-P. Shih2 1Department of Mechanical Engineering, Stanford University, Stanford, California, USA 2Department of Aerospace Engineering, Iowa State University Ames, Iowa, USA

Abstract

Despite its great importance and the tremendous efforts that have been made to understand it, turbulence remains a thorny problem. Though great strides have been made and a plethora of predictive turbulence models have been developed, there is only a modicum of guidance available on what is practical and what can be relied upon for design and analysis. This chapter seeks to provide an introduction to turbulence modeling and to make some sense of it. First, the physics of turbulence are described briefly. Then, different mathematical approaches used to predict turbulent flows are outlined, with focus on their essence, their ability to predict correctly, and their turn-around time. Next, widely used, single-point closure models for Reynolds-averaged Navier–Stokes equations are summarized, with some reference to the principles used to develop them. Finally, an appraisal is given of the current state-of-art models, in order to provide guidelines on their usefulness. The chapter concludes with comments on transition prediction.

Nomenclature

bij anisotropy tensor Cp constant pressure specific heat d shortest distance to the wall k turbulent kinetic energy or thermal conductivity P rate of turbulent kinetic energy production

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) doi:10.2495/978-1-85312-956-8/01 PrT turbulent Prandtl number 2 RT turbulent Reynolds number: RT = k /εν  ∂U  Sij mean rate of strain tensor: 1 j ∂Ui Sij =  +  2  ∂ xi ∂ x j  t time T turbulent time scale or mean temperature

Ui mean velocity component in i-direction: Ui = u i + ui = u i

Uτ friction velocity: Uτ = τ w /ρ ui fluctuating component of the velocity in i-direction xi spatial coordinate in i-direction y coordinate normal to the wall + + y normalized distance from wall: y = ρUτ y /µ

Greek α thermal diffusivity: α = k / ρ Cp = ν / PrT αT turbulent thermal diffusivity ε dissipation rate of turbulent kinetic energy κ von Karman constant θ fluctuating temperature µ, ν dynamic, kinematic viscosity µT, νT turbulent dynamic, kinematic viscosity ρ density τw wall shear stress ω dissipation rate per unit k Ω vorticity

1 Introduction

Most flows of engineering interest are turbulent. Turbulent flows are characterized by unsteadiness, three-dimensionality, and highly convoluted flow structures that span a wide range of length and time scales [1–5]. Although features of a turbulent flow can be immensely complicated at any instant of time, their ensemble-averaged mean might not be. For example, the mean turbulent flow can be steady or unsteady as well as zero, one, two or three-dimensional, with simple, smooth structures, dominated by one or two length and time scales. For most engineering applications, interest is only in the mean flow field. The goal in predicting turbulent flows for design and analysis is to account for the relevant physics by using the simplest and the most economical mathematical model possible. Economics is as important as accuracy because predicting turbulent flows can be highly intensive computationally. In engineering applications, a prediction must be not only meaningful, but also obtained in a reasonable amount of time, in order to have a chance to impact design. The objective of this introductory chapter is four-fold. The first is to give a

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) brief description of the physics of turbulence. The second is to outline different mathematical approaches for predicting turbulent flows with focus on their essence, their ability to predict correctly, and their turn-around time. The third is to provide an overview on mathematical models that are currently being used in engineering design and analysis with some reference to the principles used to develop them. The fourth is to offer an appraisal of these models and to provide some comments on transition predictions.

2 Physics of turbulence

Though a great deal has been learned over more than a century of study on turbulence, true understanding remains elusive. Without considerable empiricism we still cannot predict – even on the average – how turbulence affects lift, drag, pressure loss, or scalar mixing in most engineering systems. Advances in hot wires and non-intrusive techniques such as laser-doppler anemometry, particle- imaging velocimetry, and molecular-tagging velocimetry have provided a wealth of detailed information on turbulent flow fields. But explanations and theories that unify the observations fall far behind. That just testifies to the enormous complexity of turbulent flows. The understanding that we do have is mostly from studies of idealized situations and simple configurations. Examples include isotropic and homogeneous turbulence behind grids, jets into initially quiescent fluids, flows over flat plates or circular cylinders, and fully developed incompressible flow in high aspect ratio channels and circular pipes [1–5]. With this backdrop, a qualitative description of incompressible turbulent boundary-layer flow over a smooth flat plate with a zero pressure gradient is given here. Data from this flow are employed by most turbulence models currently in use. The purpose in providing such a description is to give a sense of what we are trying to model and what the models are intentionally leaving out. Transition: A laminar flow will become turbulent when inertia becomes sufficiently large that viscous and other stabilizing forces can no longer restrain small irregularities or perturbations to the flow field from growing into highly convoluted unsteady, seemingly chaotic, flow structures. The path from orderly laminar flow to chaotic turbulent flow is referred to as transition. Its evolution depends on whether the flow is distant from walls – such as jets – or next to walls – such as boundary layers. It also depends on what upstream and far-field disturbances are present. If the turbulence develops next to walls, then surface geometry and roughness also play major roles. To fix ideas, consider a long, but thin, smooth flat plate with a sharp leading edge and adiabatic walls, moving at constant velocity U into quiescent air at zero angle of attack and low Mach number. The boundary-layer flow that develops over the plate will have a zero pressure gradient, become steady with respect to the plate, and will remain laminar and two-dimensional from the plate’s leading edge until a critical distance downstream of the plate. That distance is where the Reynolds number Re = U x /ν , with x measured from the plate’s leading edge, exceeds some critical value. Beyond that x location, instability theory predicts

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) that Tollmien–Schlichting waves become unstable, as is verified by experiments. When the amplitude of these waves reaches about 1% of the free-stream velocity, experiments show a secondary nonlinear instability sets in, forming three- dimensional waves that are oblique to the main flow direction. These waves produce nearly periodic spanwise valleys and peaks in the amplitude of the Tollmien–Schlichting waves, which change the local velocity profiles through vortex tiliting, stretching, and compression, thus making them fuller in the valleys and less full, with inflections, in the peak regions. The velocity profiles with inflection points, being inviscidly unstable, further amplify the Tollmien– Schlichting waves, expanding rapidly the growth of the peak regions and creating localized regions of high shear through vortex stretching. When the extent of the localized regions of high shear reaches a certain level, a tertiary instability sets in. That leads to strong small-scale instabilities with high frequency disturbances, a process referred to as breakdown. If the free-stream flow is already turbulent, or if the surface over which the fluid flows is rough, then the transition to turbulence is markedly different. This is referred to as “bypass” transition because it circumvents the formation and evolution of Tollmein–Schlichting waves. Tollmein–Schlichting waves are only weakly unstable; hence, they are readily bypassed. Bypass transition can take place with less than 1% free-stream turbulence intensity. Bypass transition is discussed in section 6. Understanding transition is important for two opposite reasons. The first is to understand how turbulence can be avoided, so that drag and mixing will be less; the second is to promote turbulence, in order to ensure postponed separation or improved mixing. Nature of turbulent boundary layer: Once a flow does become turbulent, it is always unsteady, three-dimensional, and characterized by a spectrum of scales that span from the size of the problem (e.g., the diameter of the duct for internal flows or the thickness of the boundary layer for external flows) to sizes where viscous dissipation will convert mechanical energy to thermal energy. Turbulent flows can never be reproduced exactly in the detailed instantaneous flow field. Even when the geometry and the operating conditions are identical (identical in the sense of what is possible from manufacturing and flow control), the instantaneous turbulent flows will differ markedly. This is because transition and evolution of turbulent flows are highly sensitive to the smallest perturbations, which are beyond our ability to control. Fortunately, though the instantaneous turbulent flow fields can differ markedly, the mean flow fields are similar. Because of the chaotic nature of turbulent flows and because we are often only interested in the net effects of turbulence, statistical methods are used to quantify turbulent flows. These include single- and multiple-point space and time correlations. Vortex stretching is a classical explanation for the breadth of scales [5]. Random advection causes fluid particles to separate on average. If those particles are points on a vortex tube, it follows that the vortex tube will be stretched, and hence will intensify and cascade to small scale. The manner by which eddy structures enter the boundary layer has been

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) termed burst and sweep [6]. This idea is that vorticity within the viscous wall layer becomes distorted, to produce horseshoe-like perturbations. These then lift from the wall, producing elevated, unstable shear layers. The shear-layer instabilities contribute to the chaotic flow field. In principle, these phenomena underlie the well-known statistical properties of the boundary layer; in particular, the viscous sublayer, the log-law layer, and the law-of-the-wake. For most purposes, models are guided by the statistical description, not by a phenomenological description of eddies.

3 Classification of turbulence models

Since the Navier–Stokes (N–S) equations are valid for unsteady, three- dimensional (3-D) laminar flows, they are also valid for turbulent flows. These equations form the basis of most mathematical analyses of turbulence. As used here, the N–S equations include both the momentum equations and the conservation equations of mass (continuity) and total energy. Mathematical studies of turbulent flows based on the N–S equations can be classified into the following four categories: (1) those that analyze the unaveraged/unfiltered 3-D, unsteady N–S equations – known as direct numerical simulation (DNS); (2) those that analyze spatially filtered, 3-D, unsteady N–S equations – known as large-eddy simulation (LES); (3) those that analyze ensemble or density- weighted ensemble averaged N–S equations (RANS); and (4) those that use different approaches in different parts of the turbulent flow field – known as hybrid methods, an example being detached eddy simulation (DES)).

3.1 Direct-numerical and large-eddy simulations

With DNS, the turbulence is not modeled, it is simulated. The governing, Navier–Stokes (N–S) equations are solved numerically, except at the inflow and outflow boundaries where appropriate chaotic conditions must be presented. No modeling is needed because the unsteady, 3-D, N–S equations already account for all the relevant physics of turbulence. But, this simplicity in modeling is accompanied by a heavy price. Basically, each simulation must accurately resolve all length and time scales of the turbulent flow field from the largest, integral scales, to the smallest, Kolmogoroff scales. For turbulent flows of engineering interest, such as those in aerodynamics, propulsion, and power generation, the number of grid points and time steps required to resolve these scales can be unconscionably large. Since many realizations of the flow are needed before one can extract meaningful statistics, the computer time requirement can range from months to years. Thus, DNS is clearly impractical as an engineering tool. The role of DNS is to provide basic understanding and to guide the development of models needed by LES and RANS. The reader is referred to Ferziger and Peric [7] for further discussions on DNS. With LES, the relatively large-scale turbulent structures are resolved (other chapters in this volume discuss LES). The small-scale structures, referred to as

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) subgrid scales (SGS), are modeled. This approach has considerable merit because the small scales, unlike the larger ones, are nearly independent of history and boundary conditions, so that a universal model for the SGS may be possible. By modeling the SGS – instead of resolving them as in DNS – much higher Reynolds number turbulent flows can be simulated with the same computational resource. In LES, the large- and the small-scale structures in a turbulent flow are filtered from the 3-D, unsteady, N–S equations by an operation of the form,

u (r, t) = u(ς,t) G(r −ς,t;∆) dς (1) ∫D

where u is the variable to be filtered; u is the filtered variable; t is time; r = (x, y, z) is the position vector of a point in space about which filtering will take place; ∆ is the filter width; and D is the entire spatial domain. The filtering function G determines the size or width of small-scale structures to be filtered. Typically, that size or width is proportional to the local grid spacing. This is one of the weaknesses to the approach: it is inherently grid-dependent. The definition of subgrid scale varies implicitly with the grid coarseness, and there is no set criterion for accuracy. In modeling the SGS terms, the primary goal is to remove the energy transferred from the resolved scales at the correct rate [8]. Although the energy transfer can be from the smaller scales to the larger ones (backward scattering), it is predominantly from the larger scales to the smaller ones (forward scattering). Most SGS models are based on the eddy-diffusivity concept with algebraic closures for the eddy diffusivities. The popular Smagorinsky model, which assumes small scales can recover equilibrium nearly instantaneously, is inadequate in the region next to a wall; even dynamic SGS models, as suggested by Germano et al [9], that can account for nonequilibrium effects, are inadequate in the near-wall region. Typically, LES requires almost DNS resolution in order to resolve the highly anisotropic turbulence near walls. This is of special concern in heat-transfer applications. In performing LES, the need to compute accurately the smallest resolved scales, where finite-difference and finite-volume methods generate the greatest errors, cannot be over emphasized. Other computational issues are related to the inflow and outflow boundaries, where appropriate chaotic conditions must be imposed to obtain correct statistics in the interior of the domain. Though LES requires considerably fewer grid points than DNS (perhaps one to two orders of magnitude less), the number of grid points is still substantial. Also, similar to DNS, LES is always 3-D and unsteady. If the statistics of the turbulence are stationary, then flow evolution must be simulated and continually ensemble averaged until the statistics of the flow no longer change with time. If the statistics of the turbulence are non-stationary, then it is unclear how many realizations of a flow need to be simulated and how the statistics can be unscrambled to understand the unsteadiness effects such as cycle-to-cycle variations in internal combustion engines. Thus, LES, like DNS, is not intended to analyze full-scale practical flows.

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) There may be some niche applications for it, but often it is regarded as a tool to study carefully chosen flows, to guide the development of models needed by RANS.

3.2 RANS and Reynolds-stress modeling

With Reynolds averaged Navier–Stokes (RANS) equations, the 3-D, unsteady N–S equations are ensemble averaged if the mean flow is unsteady. If the mean flow is steady, either ensemble or time averaging can be used since they are then identical. For both steady and unsteady mean flows, the averaging can be single- or multi-point space and/or time correlations [1–5]. The models described later in this chapter are based on single-point averages. The unweighted time and ensemble-averaging operators are given by

1 t + p u = lim ∫ udt (2) p →∞ p t

N 1 (3) u = lim ui N →∞ ∑ N i=1 where u = u + u ′ is the variable being averaged, with u being the mean and u ′ being the fluctuation about the mean. In eqn (2), p is the period of time over which the averaging takes place. In eqn (3), N is the number of realizations of the same turbulent flow (same in the sense of identical geometry and mean boundary conditions) over which the averaging takes place. Obviously, both p and N must be sufficiently large to give meaningful averages. For flows where density fluctuations cannot be neglected (e.g., some chemically reacting flows and turbulent flows with Mach number exceeding five), density-weighted, or Favre, time and ensemble averaging are often used [1–5,10,11]. Even though turbulent flows are always 3-D and unsteady, the RANS equations can be 1-D, 2-D, or 3-D as well as steady or unsteady, depending on how the mean quantities vary. Also, the flow fields are considerably simpler and smoother, dominated by only a few length and time scales. Thus, RANS is computationally much less intensive than DNS and LES. However, RANS requires one to model correlations between the fluctuating components, such as the Reynolds stresses, that arise from the averaging process. A universally valid model is seemingly impossible, because Reynolds stresses have all scales (largest to smallest) embedded in them, and larger scales depend on the geometry and boundary conditions, which are problem dependent. Nevertheless, the RANS approach has provided meaningful results for a wide range of problems [10–21]. Despite its imperfections and shortcomings, RANS has developed into a widely used engineering tool and general-purpose RANS codes are being applied to increasingly complex and varied problems. Models for the Reynolds stresses can be classified into the following categories: differential Reynolds stress models (DSMs), algebraic Reynolds stress models (ASMs), nonlinear constitutive relations (NCRs), and eddy-

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) viscosity models (EVMs). Each category is described below with focus on the basis and limitations.

3.2.1 Eddy-viscosity models EVMs tackle the Reynolds-stress closure problem by assuming that the deviatoric part of the Reynolds stress is aligned with the mean rate of strain, and by introducing the concept of eddy viscosity as a proportionality factor. Analogous to molecular viscosity from the kinetic theory of gases, the eddy viscosity is a function of a correlation time and a velocity scale. These scales can be represented by algebraic equations or by one or more PDEs. In algebraic models, such as those due to Prandtl, Cebeci–Smith, and Baldwin–Lomax [4,5,10,11,22], the production of turbulent kinetic energy, k, is assumed to be everywhere equal to its rate of dissipation, ε. With this local equilibrium assumption, the eddy viscosity becomes a function of the local mean flow field. Differential-equation models do not make this assumption, and as a result, require one or more PDEs to compute the evolution of the scales. The velocity scale is invariably computed from the PDE for k because that equation requires minimal modeling. A PDE for the time scale is more difficult to formulate. A successful one is based on ε, resulting in the k–ε model. Another popular variation, the k–ω model, invokes an equation for an inverse time scale, ω. Though differential equation models are more general than algebraic ones, they are still plagued by deficiencies from invoking a linear stress–strain relation. The assumed alignment of Reynolds stresses with the mean strain rate incorrectly implies independence from the rate of rotation, and an instantaneous response of the Reynolds stresses to changes in the mean flow. Despite these limitations, two-equation models are widely used because of their reasonable computational cost and performance. More importantly, there is a large community engaged in testing and making improvements to these models with empirical and semi-empirical corrections

3.2.2 Algebraic Reynolds stress models To avoid solving extra transport equations, ASMs neglect the convection and diffusion terms in the Reynolds stress transport equations. This converts them from partial differential equations (PDEs) to algebraic equations. The convection and diffusion terms can be dropped if turbulent convection and diffusion are small, or if convection is approximately equal to diffusion so that they cancel. Since these assumptions are rarely valid for engineering flows, Rodi [23] proposed an alternative derivation of ASMs. His approach is based on the following two assumptions: First, convection minus diffusion of Reynolds stresses is proportional to convection minus diffusion of turbulent kinetic energy. Second, the Reynolds stress tensor normalized by the turbu lent kinetic energy is constant along a pathline; in other words, D/Dt(uiuj/k)=0. With this closure, some effects of convection and diffusion are retained, and only two PDEs are needed to compute the turbulence quantities, either k and ε or k and some other dimensionally suitable function such as ω = ε/k. In Rodi’s approach, the algebraic equations for the Reynolds stresses are implicit. However, Pope [24]

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) found explicit, closed-form solutions in 2-D, that were subsequently extended to 3-D by Gatski and Speziale [25], Abid et al [26], and Rumsey et al [27].

3.2.3 Nonlinear constitutive relations Instead of deriving a transport equation for each Reynolds stress, nonlinear constitutive relations represent Reynolds stresses by a finite series expansion [4,5,10,11,28]. First, the most general expansion for a second-order tensor function of the velocity gradient tensor is derived. In this expansion, the leading two terms represent an eddy-diffusivity model. The remaining terms are functions of the mean strain rate and rotation tensors and their invariants. Afterwards, mathematical and physical constraints along with experimental data are used to determine the closure coefficients to ensure proper behavior in limiting cases.

3.2.4 Differential Reynolds stress models Of the RANS models, DSM’s represent the highest level of closure. The major advantage of DSM’s is that they naturally account for more physics of turbulent flows, such as streamline curvature, rotation, and stress anisotropy. DSM’s also can account for the effects associated with the time-lagged response of turbulence to changes in the mean flow. With DSMs, one needs to solve six transport equations for the six Reynolds stresses plus one equation for the length scale (typically, the dissipation rate of turbulent kinetic energy ε). Depending on how velocity–temperature fluctuations are modeled, three additional transport equations may be needed. The modeling has been guided by a set of mathematical and physical arguments such as realizability, isotropy of the small scales, symmetry, consistency in tensorial representation, and kinematic constraints. Dimensional consistency between the models and the terms being modeled is achieved by using a set of scales. Typically, a single set based on the turbulent kinetic energy and its dissipation rate, k and ε, is used. From dimensional analysis, this choice leads to a velocity scale of u = k1/2, a length scale of L = k3/2/ε, and a time scale of T = k/ε, which relate to the larger eddies. Difficulties arise near to walls, where viscous scales become important. Some of these issues are discussed later in Section 4.

3.3 Hybrid models

Since LES has difficulty in modeling the near-wall region unless a very large number of grid points is used, Spalart [29] proposed the idea of detached eddy simulation (DES). This is a hybrid formulation that uses RANS in the near-wall region and LES away from walls. It hopes to bring together the best elements of RANS and LES. A single RANS model is solved throughout the flow with a clip on the eddy viscosity that makes it a subgrid model in the LES region. As in LES, the clip is based on grid spacing. In the RANS region, DES approximates the steady, ensemble-averaged solution. The LES region, however, remains unsteady and chaotic. Thus, though DES is computationally less intensive than LES, it is still much more intensive than RANS. See [29] and [30] for details on DES.

4 Single-point closure models for RANS

Since DNS, LES, and DES are extremely intensive computationally, most turbulent flows of engineering interest are analyzed by RANS, with closure models based on single-point averages. The ambition of such models is to compute directly the statistics underlying the complex, irregular flow field, without having to simulate it and then extract statistics by processing data. There are good reasons for resorting to a statistical description. The average over an ensemble of realizations of turbulent flow yields a reproducible, smooth flow field. The smoother, averaged flow is more amenable to computation than is the instantaneous, random flow. Unfortunately, there are no exact equations governing this smooth, average field; and that is where closure modeling comes into play. In Section 3.2, an overview of the RANS approach is given. In this section, the RANS approach is surveyed in greater depth.

4.1 Mean flow equations

Derivation of the single-point moment equations can be found in many references [4,5,7,10,11,22]. In constant density flow, single-point, time or ensemble averaging of the continuity, momentum (Navier–Stokes), and thermal energy equations yields the following moment equations for the mean velocity (U), temperature (T), and pressure (p):

∂U j = 0 (4) ∂x j ∂U ∂U ∂ p ∂   ∂U ∂U  ∂ i i  j i  (5) + U j =− + ν +  − (u j ui ) ∂t ∂x j ∂xi ∂x j   ∂xi ∂x j  ∂x j   ∂T ∂T ∂ ∂T ∂ . (6) + U j = α  − (θ u j ) ∂t ∂ x j ∂ x j  ∂ x j  ∂ x j

The above equations are unclosed because the averaging process introduces correlations of the fluctuating components, such as uiu j in the momentum equation and θ u j in the thermal-energy equation. The purpose of closure models is to formulate a soluble set of equations to predict turbulence statistics. These consist of moment equations, plus formulas, or auxiliary equations that express unclosed terms as functions of the dependent variables. The extra formulas must contain empiricism, often via a fairly small number of experimentally determined model constants. The empiricism introduces a degree of freedom through a choice of which data are used to set the constants. While this is discomforting, the introduction of empiricism enables closure models to predict the very complex phenomenon of turbulent flow by solving a relatively simple set of equations.

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) As noted in Section 3.2, various approaches have been proposed for modeling the correlation terms in the RANS equations. Here, only methods that add auxiliary transport equations will be surveyed, since these are the type used in most general-purpose CFD codes. The closure schemes will be categorized into those based on transport of turbulence scalars (e.g., EVMs) and those based on transport of the Reynolds stress tensor (e.g., DSMs).

4.2 Models with scalar variables

4.2.1 The k–ε model

The k–ε model seeks to predict an eddy viscosity, νT (Section 3.2.4). From 2 dimensional analysis, it can be argued that νT ∝ u T , where u is a velocity scale and T is a time scale. In the k–ε model, T ∝ k /ε and u2 ∝ k. Introducing a constant of proportionality gives

2 νT = Cµk /ε. (7)

Similarly, the turbulent thermal diffusivity is

αT = νT /PrT . (8)

where PrT is the turbulent Prandtl number. In turbulent boundary layers,

PrT ≈ 0.9; but, in turbulent free-shear layers, it is lower, of order 0.6. Equations (4) to (6) for the mean flow are solved with the scalar eddy diffusivities given by eqns (7) and (8) and the constitutive relations

2 −u u = 2ν S − kδ and −uiθ = αT ∂T ∂xi (9) i j T ij 3 ij where Sij is the mean rate of strain tensor. Equations (7) to (9), when substituted into eqns (4) to (6), close the mean flow equations. The constitutive equation given by eqn (9) is a linear stress-strain relation, as for a Newtonian fluid; nonlinear models also have been proposed at times (Section 3.2.4). The problem addressed by the k–ε model is how to predict νT robustly throughout the flow field. The exact evolution equation for k is unclosed. To close it, the turbulent self- transport and pressure-diffusion terms together are replaced by a gradient transport model to obtain

   ∂k ∂k P – ε + 1 ∂ µT ∂k . (10) + U j =  µ +   ∂t ∂x j ρ ∂ x j  σ k  ∂ x j 

The usual value of σk is 1. The exact production term is P = −uiu j Sij ; but, with eqn (9), it becomes

P = 2νTSijSij (11) for incompressible mean flow. The modeled transport equation for ε cannot be derived systematically. Essentially, it is a dimensionally consistent analogy to the above k-equation:

∂ ε ∂ ε C P − C ε    + U = ε1 ε 2 + 1 ∂ µT ∂ε (12) j  µ +   ∂ t ∂ x j T ρ ∂x j  σε  ∂x j 

The time scale T = k/ε makes it dimensionally consistent. Equation (12) is analogous to eqn (10), except that empirical constants C ε1, C ε2, and σε have been added because the ε-equation is just an assumed form. The empirical coefficients are chosen to impose certain experimental constraints. The standard values for the constants are [31]

Cµ = 0.09; Cε1 = 1.44; Cε2 = 1.92; σε = 1.3. (13)

Cε1 is a critical constant. It controls the growth rate of shear layers. A 7% decrease of Cε1 increases the spreading rate by about 25%. However, this is a little misleading, because the spreading rate is determined by C ε1 – 1, so the leading 1 is irrelevant and the change in spreading rate is nearly proportionate to

C ε1 – 1. The standard value of C ε1 was chosen to fit the spreading rate of a plane mixing layer. Slightly different values would be obtained for other flows. Cε2 is determined by the decay exponent measured in grid turbulence. The value of C µ is found from 2 measured in a log-law layer. σ is determined from the uiu j /k ε von Karman constant via a closed form, log-law layer solution [5].

4.2.1.1 Boundary conditions and near-wall modifications Although the boundary conditions for the k–ε model at a no-slip wall are quite natural, the near-wall behavior of the model is not. At a no-slip surface u = 0, so k = | u |2 /2 has a quadratic zero. Hence, both k and its normal derivative vanish. It is common practice to convert this into k = 0 and a condition on ε. As the wall is approached, eqn (10) has the limiting behavior of

∂2 k ε = ν (14) w ∂ y 2

Integrating the above equation gives k = A + By + εy 2 /2ν , where A and B are integration constants. The condition of a quadratic zero gives A = B = 0; and hence,

If the time scale T = k/ε is used, then T → O(y 2) as y → 0 and the right side of eqn (12) becomes singular in the same manner as ε2/k. Since the Kolmogoroff scale, T ∝ ν /ε , is more appropriate near the surface, the following formula can be used:

T = max (k /ε,6 ν /ε) (16) where 6 is an empirical constant of proportionality. 2 A more profound failure near the wall is that the formula νT = Cµ k /ε gives an erroneous profile of eddy viscosity. One device to fix νT consists of damping the viscosity. To this end, it is replaced by

2 νT = fµ Cµ k / ε (17)

The damping function can be dependent on either k1/2 y/ν or k2/εν, depending on the model. For example, Launder and Sharma [31] use

 −3.4  2 fµ = exp , RT = k /εν (18)  2   (1 + RT /50) 

Patel et al [32] tabulate various other low Reynolds number k–ε formulations. It is not sufficient to damp just the eddy viscosity; all low Reynolds number k–ε models also modify the ε-equation in one way or another.

4.2.1.2 Wall functions A viable alternative to near-wall damping is to formulate a simplified model for the wall region, and to patch it onto the k–ε model away from the wall. The k–ε formulation has been used to this end in an approach called the two-layer k–ε model [33]. Another method to circumvent the erroneous predictions in the near-wall region is to abandon the k–ε equations in a zone next to the wall and impose boundary conditions at the top of that zone. Within the zone, the turbulence and mean velocity are assumed to follow prescribed profiles [4,5,10,34]. This is the wall function method. Conceptually, the wall function is used in the law-of-the- wall region and the k–ε model predicts the flow field farther from the surface. The two are patched in the logarithmic overlap layer.

4.2.2 The k–ω model 2 −1 For the k–ω model, the eddy viscosity is of the form: νT = Cµ u /T . One role of the ε-equation is to provide the time scale, T = k/ε; instead, the k-equation could be combined directly with a time scale equation. It turns out to be more

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) suitable to use a quantity, ω, which has dimensions of inverse time. The k–ω model of Wilcox [10] can be written as

∂k ∂k 2 1 ∂  µ  ∂k  2νT|Sij| – kω + T (19) + U j =  µ +   ∂t ∂x j ρ ∂ x j  σ k  ∂ x j 

∂ω ∂ω 2 2 1 ∂  µ  ∂ω  2CµCω1|Sij| – Cω2ω + T (20) + U j =  µ +   ∂t ∂x j ρ ∂ x j  σω  ∂ x j 

νT = Cµ k / ω, (21)

In eqn (21), νT is Cµ times Wilcox’s value, which makes the analogy to k–ε more apparent. The k-equation is altered only by changing ε to kω. The ω-equation is analogous to the ε-equation. The standard constants are Cω1 = 5/9, Cω2 = 5/6, σω = σk = 2, and Cµ = 0.09. 2 Near a no-slip surface, eqn (20) has the limiting solution ω → 6/(Cω2 y ). This shows that ω is singular at no-slip boundaries. Nonsingular solutions also exist. Wilcox [10] proposed that wall roughness could be represented by prescribing a finite boundary value for ω. However, on smooth walls, the singular solution is usually invoked; for instance Menter [35] uses ω = 60/(Cω2 2 y1 ). A number of variants of k–ω are described in Wilcox [10]. In particular, a functional dependence on the cross product

∂k ∂ω ω 3 ∂ xi ∂xi is proposed to improve several aspects of the model. The added function reduces a spurious sensitivity to free-stream turbulence and makes predicted free-shear- layer spreading more accurate.

4.2.3 The SST formulation Menter proposed the shear–stress-transport (SST) model to overcome a few shortcomings of the basic k–ω formulation. To rectify a spurious free-stream sensitivity of the original k–ω model, Menter developed a two-zone formulation that uses k–ω near the wall and k–ε for the rest of the flow. The switch between these forms is by a smooth interpolation. Details of the interpolation formulas can be found in Wilcox [10]. To limit the Reynolds shear stress in strong pressure gradients, Menter also introduced the bound

 C k  k µ (22) νT = min C µ , F2  ω 2|Ω | 

 ∂U  1 j ∂Ui (23) Ωij =  −  2 ∂xi ∂x j  and |Ω| is its magnitude. The blending function

2  500C ν  F = tanh arg , 2 k µ (24) 2 ( 2 ) arg2 = max , 2   ω y ω y  confines this modification to the boundary layer. The important property of the above formula is to bound the eddy viscosity in flow regimes with large velocity gradient. There is a tendency for two equation models to substantially overpredict k in such circumstances.

4.2.4 Eddy viscosity transport models Two-equation models construct an eddy viscosity from velocity and time scales. It might seem prudent instead to formulate a transport equation directly for the eddy viscosity. This idea was developed into the Spalart–Allmaras (S–A) model [36]. The S–A model is simple, but has proved effective in many flows. Its formulation can be summarized as follows. Assume a priori that an effective viscosity, ν˜ , satisfies a prototype transport equation

 2  ∂ν˜ ∂ν˜     Pν – εν + 1 ∂ ∂ν˜ ∂ν˜ (25) + U j =  () ν + ν˜  + c  ∂t ∂x σ ∂ x ∂ x b 2 ∂ x j ν  j  j  j 

Aside from the term multiplying cb2, this is analogous to the k, ε, or ω-equations. For production, the dimensionally consistent form

Pν = cb1 |2 Ω˜ | (26) is invoked, with cb1 = 0.1355. The wall distance d is used for a length scale in the destruction term

2  ν˜  ˜ 2 εν = cω1   fω (ν˜ Ω( κd) ) (27)  d

The form of the function fω can be found in [36]. It was selected to provide accurate agreement to experimental data on skin friction beneath a flat-plate boundary layer. The key here is that eqn (23) governs an effective viscosity, ν˜ , rather than the

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) actual eddy viscosity, νT . ν˜ is contrived to vary nearly linearly all the way to the wall. This is achieved by deriving the formula

2 ˜ ν˜ ν˜ (28) Ω =Ω− 2 + 2 ()νT + ν ()κy ()κy for the effective vorticity in eqn (24), where |Ω| is the magnitude of the actual vorticity. A formulation with a nearly linear solution near the wall is attractive computationally; but, the real eddy viscosity is not linear near the wall. This is rectified by the nonlinear transformation

f (29) ν T = ν˜ ν ()ν˜ ν

The transforming function

(ν˜ ν)3 fν ()ν˜ ν = (30) ()ν˜ ν 3 + 7.13 was borrowed by Spalart and Allmaras [36] from an earlier model.

4.2.5 Non-linear constitutive equations Eddy viscosity models usually invoke the quasi-linear stress–strain rate relation given by eqn (9). There are obvious shortcomings to this formula. For instance, it incorrectly predicts that the normal stresses are isotropic in parallel shear flow. To a large extent, one hopes that eqn (9) will adequately represent shear stresses, as these are often dominant in the mean momentum equation. However, the normal stresses generally will not be correct, even qualitatively.

Nonlinear constitutive relations postulate that uiu j is a tensor function of the rate of strain S and the rate of rotation Ω, as well as of the scalar quantities k and

ε. Let the stress tensor be a function of two trace-free tensors; i.e., ui uj = Fij(Ω, S). The most general constitutive model for three velocity-component turbulent stresses in a two-dimensional, incompressible mean flow is provided by [23]

2 2 . . −uu = Cτ1 kTS – k I + kT [Cτ2 (S Ω –Ω S) 3

2 1 2 +Cτ3 (S – trace (S ) I)] (31) 3 where T is a turbulence time scale (e.g., k/ε). Note that in two dimensions, Ω 2 is 2 proportional to S so it does not appear in eqn 31. The linear model is Cr1 = Cµ and Cr2 = 0 = Cr3. In nonlinear models, these coefficients can be made functions of the invariants |S|2T2 and |Ω|2T2. One method to derive the functional

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) dependence is by solving an equilibrium approximation to one of the Reynolds stress transport equations. Another is simply to postulate forms for the coefficients. In three dimensions, a number of further terms are needed to obtain the most general tensor representation [37].

4.3 Tensor transport models

Anisotropy exists in all real flows. In parallel shear flows, the dominant anisotropy is the shear stress. Eddy-viscosity models are designed to represent shear stress; they are not designed to represent normal stress anisotropy. Differential stress models (DSMs), also known as second moment closure (SMC) models, are based on transport equations for the entire stress tensor. The Reynolds stress transport equation is

∂u u ∂u u 2 ∂u u u ∂2 u u i j i j Pij – δ ε + ℘ij + i j k i j (32) + Uk = ij + ν 2 ∂t ∂ xk 3 ∂ xk ∂ xk where

∂U ∂U Pij −u u i − u u j (33) j k i k ∂xk ∂xk

The self-transport is usually represented by

∂u u u   i j k ∂ ∂ . (34) =  CsTukul uiu j  ∂xk ∂xk  ∂xl 

The unclosed, redistribution tensor, ℘ij, is almost always written as a sum of rapid and slow parts. This term includes the correlation between velocity and pressure gradient, and the anisotropic part of the dissipation tensor. In homogeneous turbulence, it is usually replaced by the pressure-strain correlation

(Φij). But, in non-homogeneous flows, it is preferable to retain a single representation, ℘ij. Many of the variety of second moment closure models can be understood simply as different prescriptions for ℘ij.

Note that ε is the dissipation rate of k so that ε ≡ εii /2. Indeed, the trace of Eqn (32) is two times the k-equation. In that sense, DSM or SMC modeling can be looked on as unfolding the k–ε model to recover a better representation of stress anisotropy. The essence of DSM/SMC modeling involves developing formulas and equations to relate ℘ij to the mean flow gradients and to the dependent variable, uuij. What is needed is a dimensionally consistent function

The anisotropy tensor is defined by

u u 2 b ≡ i j − δ (36) ij k 3 ij

There is an implicit assumption of temporal locality in Eqn (35). Fij is written as the sum of slow and rapid contributions. Slow terms do not depend tensorally on the velocity gradient, ∂Ui /∂x j . Rapid terms depend on it linearly. The slow term is associated with the problem of return to isotropy. For this slow part becomes ℘ij has the form ε Fij[b, δ]. The most commonly used form for slow redistribution is the Rotta model [5]

slow ℘ij =−C1 εbij . (37)

This is just a linear relaxation of the anisotropy tensor bij toward 0. The concept that the slow term produces a tendency toward isotropy demands that C1 > 1. Typical empirical values of C1 are in the range 1.5 to 2.0. The Rotta model is usually quite effective. However, the Cayley–Hamilton theorem shows that the most general functional dependence of the slow redistribution model is

slow = m21 . (38) ℘ij −+ε Cb1ijεδ C 1 b ij −trace (b ) ij 3

m The coefficients C1 and C1 can be functions of the invariants

1 2 , 1 3 (39) IIb =− trace(b ) IIIb = trace(b ) 2 3

Speziale et al [38] invoked this form with the coefficients

m C1 = 1.7 + 0.9 P/ε and C1 =1.05 (40)

C1 was made a function of production over dissipation to incorporate data measured in shear flows. A good deal of research on DSM/SMC has focused on the rapid pressure– strain model for homogeneous turbulence. In general, the rapid contribution to eqn (35) is represented as

The various closures amount to different prescriptions of the tensor M. Constraints on M can be derived, but there is still considerable freedom in how to select it. The most common models assume M to be a function of the anisotropy tensor b. One way to devise a model is to expand in powers of anisotropy, treating coefficients as empirical constants; an elaboration is to treat the coefficients as functions of the invariants of b [39]. When the coefficients are constants and the expansion stops at the linear term in b, the general linear model (GLM) is obtained.

rapid 2  ∂U ∂U j   ∂U j ∂U 2 ∂U  ℘ij = k i +  + kC b + b i − δ b l    2  ik jk ij kl  5  ∂ x j ∂ xi   ∂ xk ∂ xk 3 ∂ xk   ∂ U ∂ U 2 ∂ U  + kC b k + b k − δ b l  (42) 3 ik jk ij kl   ∂ x j ∂ xi 3 ∂ x k 

Special cases of the above equation are the LRR [40] and IP (Isotropization of Production) models. The IP model uses

C2 = 3/5 and C3 = 0 (43)

Speziale, et al [38] found that data on shear flow anisotropy were fitted by C2 = 0.4125 and C3 = 0.2125. They also added the term

* (44) −Cs bijb ji /2 kSij

* with Cs = 0.65 to eqn (42). Adding such a term is consistent with the expansion to first order in anisotropy. This is an instance of what can be called the general quasi-linear model. Closure of eqn (32) is effected by replacing ℘ij by the sum of a rapid and a slow model. Physical processes are captured largely through the production tensor, which is exact. The redistribution formulae partially counteract production. For instance, in parallel shear flow turbulent energy is produced in the streamwise component of intensity and fed to the other components by the redistribution model.

4.3.1 Near-wall modeling These basic closure models prove to be quite incorrect near to boundaries. The problem can be dismissed by invoking wall-function boundary conditions in the log-law layer, and avoiding the near-wall region. At the first grid point, values of uiu j are set from experimental data. This is a common approach in practical computations. But, it can also be quite inaccurate in non-equilibrium flows.

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) More elaborate methods exist, which extend the model to the wall. The elliptic nature of wall effects was recognized early in the literature on turbulence modeling and has continued to influence thoughts about how to incorporate non-local influences of boundaries. In the literature on closure modeling the non-local effect is often referred to as “pressure reflection” or “pressure echo” because it originates with the surface boundary condition imposed on the Poisson equation for the perturbation pressure.

4.3.1.1 Wall echo The redistribution term, ℘ij, must be corrected for the wall influence. In the wall echo approach, it is taken to be an additive correction

h w ℘ij = ℘ij + ℘ij (45)

h The ℘ij term represents one of the models developed above, such as the general linear model given by eqn (42) plus the Rotta return to isotropy given by eqn (37). w The additive wall correction ℘ij is often referred to as the wall echo contribution. It is modeled as a function of the unit wall normal nˆ and of the shortest distance to the wall, d. A simple example is the formula

w w ε 2 L ℘ij = −C (u u nˆ nˆ + u u nˆ nˆ − u u nˆ nˆ δ ) 1 k i m m j j m m i 3 m l m l ij d

w rapid rapid 2 rapid L −C2 (℘im nˆ mnˆ j +℘jm nˆ m nˆ i − ℘lm nˆ mnˆ lδij ) (46) 3 d

3/2 proposed by Gibson and Launder [41]. Here, L = k /ε, and nˆ i are components of the unit wall normal vector. The factor of L/d causes this correction to vanish far from the surface. The idea is that the wall affects decay at a distance on the order of the correlation scale of the turbulent eddies. Gibson and Launder [41] used eqn (46) in conjunction with the IP model for ℘rapid. The model constants w w C1 = 0.3 and C2 = 0.18 were suggested.

4.3.1.2 Elliptic relaxation Elliptic relaxation proposed by Durbin [42] is a rather different approach to wall effects. It is incorporated by solving an elliptic equation. Connection with homogeneous redistribution models, such as those described in Section 3.3, is made via the source term in the elliptic equation. The particular equation is of the modified Helmoltz form; i.e.,

2 2 qh L ∇ fij − fij = − ℘ij /k (47) with ℘ij= k fij. On the right side, the superscript qh acknowledges that this is the quasi-homogeneous model. This approach is also used in the v2–f model, where f is a scalar. In that case the source is modeled by the IP form, and eqn (47) becomes

 2  2 2 v 2 P L ∇ f − f = C1  −  − C2  k 3 k

Solving this elliptic equation introduces non-local wall effects. The function f can be considered as a “redistribution” of turbulent kinetic energy into v2; it appears in

2 2 2   2  ∂v ∂v v 1 ∂ µT ∂v + U j = kf−ε +  µ +   ∂t ∂ x j k ρ ∂ x j  σ k  ∂ xj

This is quite analogous to the k-equation, eqn (10). Indeed, eqns (10) and (12) are also solved in this model . The elliptic relaxation procedure given by eqn (47) accepts a homogeneous redistribution model on its right side and operates on it with a Helmholtz type of Green’s function, imposing suitable wall conditions. The net result can be a substantial alteration of the near-wall behavior from that of the quasi- homogeneous redistribution model. The length scale in eqn (47) is prescribed by analogy to eqn (16) as

 3/2 3 1/4  k  ν   (48) L = max c L , cη     ε  ε  

The Kolmogoroff scale is used for the lower bound in the above equation. In fully turbulent flow it has been found that Kolmogoroff scaling collapses near- wall data quite effectively.

5 Appraisal of single-point closure models

Many attempts have been made to appraise the predictive accuracy and effectiveness of turbulence closure models. These efforts tend not to be definitive; rarely are models dismissed unequivocally. Our survey of a few of these assessments includes discussion of formulations that have been found to be lacking. Our discussion is framed in terms of broad classes of model. Numerous variants within the broad classes can be found in the literature. This is a reflection of the degrees of freedom introduced by empirical content. For instance, model constants are not constants of nature. They are calibrated to particular data. Such empirical content should not distract from the fact that the number of basic formulations is few: eddy-viscosity transport; scalar equation methods, invoking two or three transport equations; and full Reynolds stress tensor transport. The second two categories include another equation, for either ω or ε, to determine a turbulent time scale. The v2-f model includes an elliptic equation for wall effects. Near-wall modeling plays a dominant role in applications to heat transfer and

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) flow separation. That is because turbulent mixing is suppressed near the wall, so this region exerts a controlling influence on the rate of transport of momentum and heat to the surface. Near-wall modeling adds further degrees of freedom. Models again can be classified into a few varieties such as wall functions, damping functions, and elliptic relaxation [5]. Factors that have been used to distinguish the models in various tests are their ability to predict flow separation, surface heat transfer, or the influence of surface curvature and system rotation. Transition from laminar to turbulent solution branches has also been the subject of testing [43].

5.1 Scalar models

Most practical flow prediction is done with scalar, eddy viscosity models. Reynolds stress and heat flux are represented by the linear constitutive relations

2 −u u = 2ν S − kδ (49) i j T ij 3 ij −1 ∂T (50) −θui = PrT νT ∂ xi

Scalar models are distinguished by the method of predicting νΤ. Bardina et al [44] compared two-equation and eddy-viscosity transport models, primarily on their ability to predict adverse pressure gradient and shock induced separation. They favored the SST and S–A models over k–ω and low Reynolds number k–ε. The latter was quite inaccurate. S–A was preferred on numerical grounds and because of its satisfactory predictive accuracy. In several other studies, k–ε with wall functions was found unreliable for predicting flow separation. Its tendency is to predict attached flow beyond the point at which the boundary layer should separate. Iacovides and Raisee [45] and Gu et al [46] opined that heat transfer prediction argues for integration to the wall, as opposed to using wall functions. Their tests were on flow in ribbed, curved channels. These are relevant to internal cooling of turbine blades. Again, low Reynolds number k–ε was not recommended, especially if rotation is involved. They advocated full Reynolds stress transport models. Heat transfer predictions are strongly dependent on the eddy viscosity distribution very near to the surface as noted by Ooi, et al [47]. In a flat plate + boundary layer, νT becomes equal to ν when y ≈10. If a model predicts the eddy viscosity to be higher than the molecular viscosity significantly below this y+ value, then heat transfer will be over predicted. Ooi, et al [47] shows that both the S–A model and the two-layer k–ε model severely underpredict heat transfer for flows in a ribbed, square duct. This could be traced to underprediction of νT adjacent to the wall. The v2-f model was found to be more accurate. The ribs in this geometry caused complex secondary flows that present a challenge to turbulence models.

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) Poor heat transfer predictions are also found with k–ε in impinging flows. This is largely due a severe overprediction of k under high rates of strain. Vieser, et al [48] have obtained good predictions, similar to those of v2-f, for impinging jets and other heat transfer cases with the SST variant of k–ω/k–ε. There is an emerging sentiment that, among two-equation formulations, SST tends to be more satisfactory than k–ω or k–ε, although various modifications are needed of the basic formulation; most notably a fix is needed to prevent anomalously high k under high rates of strain [10,14-17]. Flexible wall treatments [48] allow SST to be used with log-law layer boundary conditions, or integrated to the wall, depending on grid resolution.

5.2 Tensor models

Approaches that evaluate the Reynolds stress with more complex methods than eqns (49) and (50) will be lumped under the present heading. These include nonlinear constitutive relations (NCRs), algebraic Reynolds stress models (ASMs), and differential Reynolds stress models (DSMs). It has been proposed that nonlinear constitutive relations to compute the Reynolds stress tensor might provide an improvement of scalar models. The 2 non-linearity can be both tensoral, involving Sij or other non-linearity in the velocity gradient tensor, or scalar, replacing constant coefficients by functions. Apsley and Leschziner [37] report on a collaborative assessment of closure schemes. Two- and three-dimensional separation were tested using both an asymmetric diffuser and, in a companion study, a generic wing-body junction. The gamut from quasi-linear eddy viscosity, to nonlinear constitutive equations, to full Reynolds stress transport, was considered. Tensorially nonlinear constitutive formulations proved to be of limited value. The primary finding was that the linear constitutive relation given by eqns (49) and (50), modified by adding strain-rate dependence to Cµ constitutes a more effective formulation. For example, the SST model, as an instance of modified C µ in the νT formula, is a great improvement over the native k–ω formulation. In this case, response to pressure gradients becomes more accurate by (in effect) making C µ depend on the rate of strain. As in other studies, Apsley and Leschziner [37] found k–ε formulations to be unsatisfactory. Lin et al [49] applied the explicit ASMs of Abid et al [26] to study a smooth duct of square section with a 180ο bend under rotating and non-rotating conditions. They found the model to predict secondary flows in the four corners of the duct from anisotropy of the Reynolds normal stresses. However, similar to the finding of Rumsey et al [27] in a two-dimensional study, this class of ASMs is unable to predict fully the enhanced turbulence from concave walls and the suppressed turbulence from convex walls. Nonlinear constitutive relations have not proved effective in a robust manner, but strain-dependent eddy viscosity limiters, or time scale bounds, are very useful. In their study of internal cooling of turbine blades, Iacovides and Raisee [45] favored DSMs because they respond to curvature in a natural way. It is safe to say that when such effects significantly

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) influence the turbulent intensity, Reynolds stress transport formulations are more reliable than scalar formulations. For instance, suppression of turbulent energy in a strongly swirling flow can be captured. The SSG model [38] was developed specifically to account for effects of system rotation. Unfortunately, the near-wall treatment for DSMs remains problematic. A wall echo formulation was invoked by Iacovides and Raisee [45] in their DSM. They added some terms to the Gibson and Launder model [41]. For this reason, evaluations of this type are of limited guidance. Those that adhere to widely used forms of the considered models are more suited to advisory tasks. Iacovides and Raisee [45] concluded that some such form of wall treatment is preferred to wall functions, but that the Gibson and Launder [41] wall echo formulation is not reliable. Elliptic relaxation [42] provides a more generic, and often more accurate, approach but adds expense. Suffice it to say that near-wall treatments for DSMs are too uncertain to warrant a recommendation on which is most effective. In practice, it is common to use wall functions, or a variant of the two- layer approach. Similarly, many comparisons between DSMs are targeted on the virtues of a new variant, rather than assessing the capabilities of existing models. A broad division is between models invoking a quasi-linear rapid pressure-strain closure (e.g., IP, SSG; see Durbin and Reif [5]), and those invoking non-linearity (see Craft and Launder [50]). At present, the former, simpler form of model is generally preferred. Scalar models are insensitive to centrifugal stabilization of the turbulence, and this has been shown repeatedly to favor DSMs. It should be mentioned, however, that scalar models can be modified to incorporate rotational stabilization. Such approaches are less reliable than DSMs, and are done for simplicity. When influences such as swirl or streamline curvature have a secondary effect, scalar models can be as effective as DSMs. For instance, swirling flow in a U-duct has been predicted adequately by a scalar model because centrifugal and pressure gradient effects on the mean flow are captured by the RANS equations, and the direct swirl effect on the turbulence were secondary [14].

5.3 Unsteady RANS

Turbulence models represent averaged effects of essentially random eddy motion. Nevertheless, confusion exists between ensemble and time averaging. For this reason, it has become common to refer explicitly to unsteady RANS when time-dependent flow is computed. When the unsteadiness is periodic, the frequency spectrum contains a spike at the coherent frequency and its harmonics, plus a broad-band level. The turbulence model represents the broad-band component; the spikes are part of the mean flow and hence must be computed explicitly. Often a steady solution can be obtained by imposing a symmetry condition on the calculation. However, when the flow is not statistically steady, the steady solution underpredicts mixing and overpredicts the length of wakes [51]. Errors associated with steady calculation of unsteady flows are mistakes in

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) the mean flow, and should not be attributed to the turbulence model. One error that can be caused by a turbulence model is to produce steady flow when unsteady shedding should be present. There is a tendency for k–ε with wall functions to have this fault. Many cases exist in which improved predictions have been obtained by unsteady RANS computations [51]. This leads into a rather gray area. Sometimes, unsteady RANS simulations seem to contain random – that is irregular − unsteadiness [52]. This is not consistent with the notion that the Reynolds average is being modeled. Nevertheless, this unsteadiness improves agreement with experiment, especially when mixing in detached shear layers is at issue. Work is needed to clarify this type of simulation. At present, it tends to be idiosyncratic.

6 Transition prediction

In the presence of more than about 0.5% free-stream turbulence intensity, boundary-layer transition bypasses linear instability mechanisms. Through experiments, computer simulations and linear theory, a phenomenological picture of the bypass route has been clarified [53,54]. Relatively low-frequency components of the free-stream turbulence are able to penetrate the boundary layer. They produce long streaks of low- and high- speed streamwise velocity perturbation near the wall. These can be thought of as forward and backward perturbation jets; that is, the streamwise velocity perturbation is the largest, and contours of streamwise velocity are elongated in the flow direction. Higher frequencies are prevented from penetrating the boundary layer by the mean shear. This sets the stage for the subsequent transition. The backward perturbation jets are associated with upward moving fluid. They lift away from the wall, where they experience perturbation by the external eddies. Transition begins in localized regions on the elevated shear-layer when it is near the top of the boundary layer. Once it begins, instability grows rapidly at these spots, filling the boundary layer with a turbulent patch. The occurrence of turbulent patches is irregular in space and time. Does this imply that statistical modeling can be applied? The possibility that RANS closure models can imitate the process of transition from laminar to turbulent flow has intrigued researchers. It can be shown that below a critical Reynolds number, some models have a stable laminar solution. Above this critical Reynolds number, the laminar solution is linearly unstable and the model bifurcates to a turbulent solution. The analysis is readily performed for the S–A and k–ω models in channel flow. S–A loses stability at Re ≈ 22 and k–ω at Re ≈ 235, which are considerably lower than the experimental value of approximately 975. Other models are not amenable to linear analysis because they cannot be linearized about laminar flow. However, there is a tendency to transition well below experimental transition Reynolds numbers. The prospect then exists for the critical Reynolds number to be delayed by revision to the model, such that its predictions in the fully turbulent region are not altered. In the case of the k–ε model, low Reynolds number

WIT Transactions on State of the Art in Science and Engineering, Vol 15, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) damping functions inadvertently play this role. A good deal of operational testing has been done. The ERCOFTAC T3A data set [55] is a widely used benchmark. Savill [43] showed that some low- Reynolds number k–ε models have a plausible ability to emulate the transition from laminar to turbulent skin-friction curves in a flat plate boundary layer. However, as noted above, these models tend to be quite inaccurate at turbulent flow prediction. Wu and Durbin [56] showed an ability of the v2-f model to emulate transition in a similar case of wake-induced transition. However, the response to pressure gradients tends to be too weak. A different approach is to incorporate an empirical transition criterion, such [57] as a correlation for the critical momentum thickness Reynolds number (Rθ) . This approach is difficult to apply in three-dimensional, general geometry computations, where the momentum thickness, θ is difficult to evaluate. However, it is currently the most reliable method to incorporate bypass transition into CFD analysis. At this stage, it can be concluded that turbulence models have shown an intriguing ability to imitate transition. This should be understood as a stability property of the model equations that can be built on to improve their operational capabilities.

References

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