An improvement to the kε turbulence model is presented and is shown to lead to better agreement with data regarding supersonic base flows. The improvement was achieved by imposing a gridindependent realizability constraint in the LaunderSharma kε model. The effects of compressibility were also examined. The numerical results show that the modified LaunderSharma model leads to some improvement in the prediction of the velocity and turbulent kinetic energy profiles. Compressibility corrections also lead to better agreement in both the turbulent kinetic energy and the Reynolds stress profiles with the experimental data.
1. Introduction
Supersonic base flows are characterized by several complex turbulent flow features as benchmark test problems in the aerodynamic drag prediction
[1
,
2]
. These flows are characterized by separating boundary layers that interact with the recirculating flow leading to a recompression region and a wake region
[3]
. Accurate prediction of such flow features requires advanced turbulence models, which include both the compressibility effects
[4

5]
and the nonequilibrium effects of turbulence
[6]
. For twoequation turbulence models, improvements in predictions have been achieved by reducing the production of the turbulent kinetic energy, or by increasing the dissipation rate of kinetic energy.
One of the important requirements for good turbulence models is the realizability condition that is not usually satisfied directly in any linear eddyviscosity formulation
[7]
. Several researchers
[8

12]
have found that the realizability constraints could be fulfilled by decreasing the eddy viscosity, and that nonlinear eddy viscosity models or weakly nonlinear eddy viscosity formulations improve the performance of the turbulence models for flows in the presence of adverse pressure gradients, particularly involving shock wave/boundarylayer interactions. The realizability condition has been considered in several ways. For the kω SST model
[11
,
12]
, the eddy viscosity formulation was derived based on the results of Bradshaw et al.
[7]
in the adverse pressure gradient regions. Coakley
[8]
and Durbin
[9]
proposed fundamentally identical corrections. The essential points regarding these corrections are to reduce the magnitude of the eddy viscosity and to produce an asymptotic behavior of the eddy viscosity coefficient when the mean strain rate leans toward infinity.
Several successful implementations of kε turbulence models
[13

16]
have also been made by reducing the eddy viscosity. A nonlinear eddy viscosity model of Craft et al.
[15]
used an eddy viscosity coefficient that is a function of the strain rate or the magnitude of vorticity and turbulent Reynolds number. Though the eddy viscosity function results in a reduction of the magnitude for the eddy viscosity when the mean strain rate is large, the function is essentially chosen to model the nearwall effects and to optimize the coefficients based on experimental data or Direct Numerical Simulations (DNS). Barakos and Drikakis
[16]
argued that the success of the cubic nonlinear eddy viscosity is based on the functional coefficient used, not on the nonlinear cubic expansion of the shear stresses. Therefore, an improved formulation for the kε turbulence model is likely to be achieved with the proper implementation of the realizability condition, rather than in the development of complex higher order constitutive relations.
In addition to turbulence modeling, spatial discretization schemes play an important role for the accurate prediction of base flows, since regions of high pressure and density gradients over a wide range of Mach numbers exist. The previous work
[11]
for the transonic flow past airfoils showed that the velocity profiles obtained from the secondorder accurate spatial discretization of the turbulence variables are more accurate than the results obtained from the firstorder accurate discretization of the turbulence equations. From the numerical experiments, the difference in the results obtained with schemes of different spatial accuracy was observed to be comparable to the differences in the results obtained with variants of the kε turbulence models.
In the present paper, several turbulence models have been examined. They include the variants of the LaunderSharma kε model
[13
,
14]
adopting the eddy viscosity formulation of Craft et al.
[15]
. The performance of these various models was examined for supersonic base flows. The compressibility modifications
[3

6]
for the kε turbulence models were also investigated. Based on the results obtained, a simple modification based on the realizability principle is proposed to the LaunderSharma model, and the prediction capability of the improved model was demonstrated.
2. Governing Equations
In the present work, the compressible NavierStokes equations and the kε turbulence equations were considered. The NavierStokes equations are
where q is the flow variable vector, and fj and fvj are the inviscid and viscous fluxes in each direction,
Here
ρ
and p are the density and pressure,
u_{i}
are the Cartesian velocity components,
E
is the total energy, and
H
=
E
+
p
/
ρ
is the total enthalpy. The quantity
τ_{ij}
and
τ
*
_{ij}
are the laminar and turbulent stresses, respectively, and qj represents the total heat flux in each direction. These quantities are defined as:
where
γ
is the ratio of specific heats and
R
is the gas constant. The variables Pr
_{l}
and Pr
_{t}
are the laminar and turbulent Prandtl numbers, respectively. The quantity
μ_{l}
is the molecular viscosity determined by the Sutherland law and
μ_{t}
is the eddy viscosity, based on the turbulence model used, which is defined later. The term
S_{ij}
is the velocity strain rate tensor defined as:
 2.1 The kε models
 2.1.1. The LaunderSharma kε model[13,14]
In the
k

ε
turbulence model as originally proposed by Launder and Sharma, the turbulence equations can be written as
where
and the convection and diffusion terms of the turbulence equations are expressed as
where S is the mean strain rate and the eddy viscosity is written in terms of
k
and
as
In the LaunderSharma model, the eddy viscosity function is written as
where
In the above equations, ReT is the turbulence Reynolds number and the term D and E model the nearwall effects. The pressuredilatation term
will be described later. The term D models the ‘anisotropic part’ of the dissipation rate where
is the ‘isotropic’ dissipation rate:
The lowReynolds term E is expressed as
The closure constants of the LaunderSharma model are
σ_{k}
=1.0,
σ_{ε}
=1.3,
α
=1.44,
β
=1.92. The damping function
f_{ε}
is defined by
In this paper, the term
c_{μ}
is defined to include the Reynolds numberdependent damping term (
f_{μ}
shown in
[14]
). The details concerning the preceding turbulence closure may be found in the original references
[13

15]
.
In order to stabilize the computation and prevent excessive turbulent kinetic energy, we impose a direct limiter on the LaunderSharma model as
This realizabilitylike limiter is applied at every iteration step as in
[11]
and has an effect similar to the realizability condition.
 2.1.2 A linear version of the kε Craft model[15,16]
Craft
et al
.
[15]
devised a nonlinear eddy viscosity model, which employed a suitable cubic stressstrain relation (not shown here) and a function cμ based on the strain and vorticity invariants:
The function cμ was optimized so that the predicted variation of the Reynolds stresses with strain rate is in good agreement with both experimental and direct numerical simulation data from a homogeneous shear flow. In the present linear version, Eq. (15) is used in the eddy viscosity expression but with a linear stressstrain relationship in Eq. (16). Craft
et al
. also modified the term E in order to reduce its dependence on the Reynolds number at low turbulent Reynolds numbers, but this modification is not used in the present implementation for simplicity.
 2.1.3 A modified LaunderSharma (LS) model
The
k

ω
models were tested for unseparated and separated transonic flows to examine the effect of the weakly nonlinear eddy viscosity model of Wilcox and Durbin (WD+ model)
[10
,
11]
. Numerical results showed that the WD+ model using the weakly nonlinear eddy viscosity exhibited better overall performance compared to the linear Wilcox model
[17]
and the SST model. Our numerical experiments show that the realizability condition improves the accuracy of predictions as well as enhances the robustness by preventing unphysical turbulent kinetic energy for transonic and supersonic flows. As shown by Gerolymos
[14]
, in order to stabilize the computations,
k
and
ε
should be bounded by positive cutoff values. The
k

ε
computations often begin from a previous computation using a robust turbulence model in order to achieve an initial flow field. These approaches are problemdependent or sometimes impractical with regards to complex geometries. The instability in the initial phase of computations can be successfully prevented by applying the realizability condition to
k

ε
turbulence models. Therefore, we can improve the LaunderSharma model by incorporating a dependence on the mean strain rate within the variation of the
c_{μ}
function. This is equivalent to adding a realizability constraint to the LaunderSharma
k

ε
model. Therefore, the following realizability condition is applied to the LaunderSharma
k

ε
model:
Equation (17) dramatically improves the robustness of the LaunderSharma model without the need for applying direct limiters, such as Eq. (14), to turbulence quantities. Equation (17) can be viewed as another form of the realizability constraint: it accepts the LaunderSharma eddy viscosity so long as it is wellbehaved, but limits it with a realizability model once the eddy viscosity exceeds some bounds. This will be referred to as the modified LS (MLS1) model.
Barakos and Drikakis
[16]
examined the performance of the cubic nonlinear eddy viscosity models and found them to be superior to the linear models based on the linear stressstrain relations. They argued that the success of the Craft et al. model
[15]
comes from the functional cμ expression utilized, and not from the nonlinear cubic expansion of the shear stresses. Based on their arguments, it would be worthwhile to use a functional
c_{μ}
in conjunction with a linear twoequation model. To investigate the effect of the functional
c_{μ}
, the variation of the eddy viscosity with the turbulent Reynolds number is displayed in
Fig. 1
(a) at different strain rates, and shows that for the Craft model (Eq. (15)) the eddy viscosity is reduced with the strain rate S. This implies that the
c_{μ}
functional in the Craft model acts like the realizability condition, though it is not directly derived based on realizability considerations.
Figure 1
(b) shows that at higher strain rates the resulting
c_{μ}
in Eq. (15) is actually smaller than that in Eq. (17) based on the realizability condition. Indeed, for this reason, the Craft model is fairly robust and behaves better than other wellknown variants of the
k

ε
models. It should be noted that
c_{μ}
of the Craft model,
Variation of eddy viscosity coefficient: (a) eddy viscosity with turbulent Reynolds number at specified strain rate, (b) eddy viscosity coefficient with mean strain rate.
which was optimized from the DNS and numerical data, exceeded 0.09 at the region of small strain rates. The present numerical experiments show that this aspect provides good velocity distributions in the recirculating region.
The obvious difference between the expression proposed here and that proposed by Craft et al. is that Eq. (15) is inversely proportional to the
and Eq. (17) is inversely proportional to
respectively. This difference might cause a different behavior in the TKE production term in each model. We follow the analysis noted by Thivet et al.
[18]
in order to explore potential improvements in regards to the realizability constraint. When crossing a shock wave normal to the x direction, the strain rate behaves like:
The production Pk, which, summed up over the cell volumes, can be written as:
We can easily find the production term of the
k

ε
model behaves as 1/Δ
x
in the cell crossed by the shock wave, when Δ
x
goes towards zero, and if
c_{μ}
is held constant. This behavior may cause grid dependence and result in excessive turbulence production for some situations. This dependence can be eliminated by imposing a cμ function based on the realizability condition, such as Eq. (15) or Eq. (17). Then, for a large mean strain rate, the production terms are
The results show that the production rate of the linear Craft model is proportional to √Δ
x
for a large mean strain rate and that the rate of the modified LaunderSharma model proposed here is independent of mesh size. This also implies that unlike the modified LS Model, the TKE production of the linear Craft model could be unphysical since it does not guarantee a positive value of the TKE production when the flow crosses a shock wave. From this, we attempt to modify the function of Eq. (15) so that the production rate is independent of the mesh size. This modified expression termed as the MLS2 model here is given by:
As shown in
Fig. 1
(b), the resulting formulation, Eq. (22), follows the variation of the Craft model at low nondimensional strain rates and the realizability bound at high strain rates.
 2.2 Compressibility Modifications
In order to account for the compressibility effects, the models of Sarkar
[4]
, Wilcox
[5]
and Ristorcelli
[6]
are considered; they account for the dilatational dissipation, which represents the added rate of dissipation regarding turbulent kinetic energy. The dissipation rate ε in the k equation can be split into a solenoidal part and a dilatational part, c, which is defined by the compressibility modification used:
 2.2.1 Sarkar model
where
M_{t}
= √2
k
/
a
is the turbulent Mach number and
a
is the speed of sound. The pressure dilatation term (Eq. (8)) is modeled as
 2.2.2 Wilcox model
Here, the compressibility term is modeled as:
where H is the heaviside step function.
 2.2.3 Ristorcelli model
Ristorcelli
[6]
proposed a compressibility modification for dilatational dissipation and pressure dilatation through statistical data and theory. Though his model has a number of heuristic constants and complex terms (not shown in this paper), it is shown to distinguish the characteristics of the compressible free shear layer from that of the boundary layer. Here, a simplified model is used, which has the leading terms of the original Ristorcelli model:
where
and
As shown in Eq. (26) and Eq. (27), the dilatational dissipation is very small
compared to the other compressibility modifications and the pressuredilatation term is dominant when the flow is in a nonequilibrium state.
3. Numerical Methods
The governing equations in the physical coordinate system were transformed into computational bodyfitted coordinates and were discretized by a cellcentered finite volume method. The HLLE+
[19]
and the thirdorder MUSCL schemes
[20]
were used with the minmod limiter to obtain secondorder spatial accuracy. Central differencing was applied to obtain variable gradients of the viscous flux. As discussed in
[11]
, a secondorder scheme for the turbulence variables produces more accurate flow predictions compared to those from a firstorder scheme for separated flows. To enhance the robustness for supersonic flows, the same MUSCL secondorder scheme, which was used for the NavierStokes solver, was also employed for the turbulence variables.
The diagonalized alternatingdirection implicit (DADI) method was used as the solver to determine the steadystate solutions
[11]
. It should be noted that the contribution of the viscous terms cannot be simultaneously diagonalized, in contrast to the inviscid terms, and it was added in the implicit part only through an approximation of spectral radius scaling. An algorithm was used to integrate the NavierStokes and the turbulence equations sequentially. In the present implicit algorithm, the turbulence equations were iterated only once per time step because more iterations do not reduce the total computing time for the implicit method. The source vectors for each turbulence model were treated implicitly, because otherwise, it would have resulted in a stiffness problem in the timemarching methods. The contributions of the turbulent dissipation terms were added in the implicit parts to increase the diagonal dominance, whereas the production contributions were treated explicitly. Additional details regarding the numerical scheme used to solve the NavierStokes and the turbulence equations can be found in
[11]
and the source term linearization method is well documented in
[21]
.
Boundary conditions affect the accuracy as well as the
Schematics of supersonic base flow and boundary conditions.
U velocity along the centerline and base pressure distributions with grid resolution.
convergence of the numerical scheme. At the solid walls, noslip conditions for velocities were applied and the density and energy were extrapolated from the interior cells. The value of k and
was set to zero at the wall for the present
k

ε
turbulence models.
4. Numerical Results and Discussion
To examine the performance of the turbulence models, a turbulent supersonic flow
[1

3]
past an axisymmetric base was studied. The freestream conditions were
M
_{∞}
= 2.46 and
R_{e}
= 5.2×10
^{7}
/m based on the freestream velocity and the base diameter. A schematic of this flow is shown in
fig. 2
. Detailed experimental data for this flow condition and geometry are available
[1]
. The inflow velocity distribution was prescribed and obtained from EDDYBL code of Wilcox’s
[22]
.
Results were obtained with the four 2equation turbulence models discussed earlier: (1) the LaunderSharma
k

ε
model (LS) with the direct limiter, Eq. (14), (2) the Craft model which denotes the LaunderSharma model with
c_{μ}
formulation of Craft et al., Eq. (15), (3) the present modified LaunderSharma (MLS1) model with the realizability condition, Eq. (17), and (4) the MLS2 model with a new functional expression, Eq. (22), which combines the realizability constraint with the Craft formulation. Representative results from these 4 different turbulence models were compared with experimental data and presented in this section.
The computational grid consists of two blocks with 33×65 and 321×213 node points in each block. The smaller block was upstream of the step while the larger block was downstream. For grid independence, a coarse grid that was made of 21×51 and 201×150 node points was also used. The grids were stretched toward the wall in order to resolve the laminar viscous sublayer. Centerline velocity and radial pressure profiles at the wall for the coarse and fine grids are shown in
Fig. 3
, respectively. Results were shown for two turbulence models (the Craft model and MLS2). The centerline velocity and radial wall pressure results on the two grids were nearly identical. The fine grid results were therefore expected to be grid independent. Unless otherwise stated, results were presented from the fine grid.
Figures 4
and
5
display field contours of Mach number and compressibility factor, (1+M
^{2}
_{t}
), for the MLS2 model. The free shear layer separates the supersonic region from
Mach number contour ( MLS2 model ).
Compressibility factor (1 + M^{2}_{t} ) contour (MLS2 model).
the recirculating region. The Mach number contour shows an expansion at the corner and recompression of the main stream. Sharp velocity gradients of the free shear layer cause the production of turbulent kinetic energy (TKE). The maximum value of the turbulent Mach number (M
_{t}
) is approximately 0.4 at the
X
/
R
= 3.4 and the distribution implies that the compressibility modification cannot be ignored.
Figure 6
shows the pressure distributions along the base for different turbulence models. The base pressures predicted by the
k

ε
models are compared with the experimental data
[1
,
3]
, which are relatively constant with respect to the radial distance. The base pressure predicted by the LS model shows a relatively larger variation in magnitude along the base, in contrast to the nearconstant experimental data. The result using the MLS2 model shows a smaller variation in the base pressure, though the values were lower than those of the experimental data. The Craft model exhibits the closest agreement with experimental data compared to the other models.
The streamwise velocity distributions along the centerline
Pressure coefficient distributions on the base with radial distance from centerline.
U velocity distributions along the centerline.
Axial velocity profiles: (a) X/R=0.079, (b) X/R=1.26, (c) X/R=2.67.
Radial velocity profiles: (a) X/R=0.079, (b) X/R=1.26, (c) X/R=2.67.
TKE profiles: (a) X/R=0.079, (b) X/R=1.26, (c) X/R=2.67.
are shown in
Fig. 7
for the various turbulence models, and demonstrate the advantages of the present formulation. The Craft model gives better agreement with the experimental data compared to the other
k

ε
models. It is interesting to note that
c_{μ}
of the Craft and MLS2 models exceeded 0.09 at small strain rates and produced larger eddy viscosity in the recirculating region of this flow than those from the LS and MLS1 models. This increase in the eddy viscosity resulted in an increase in turbulent mixing and the reduction of the reverse velocity in the recirculation region.
Figures 8
and
9
display U and V velocity profiles at specified streamwise locations. The turbulent kinetic energy profiles are compared in
Fig. 10
. The
k

ε
Craft model and the MLS2 model predictions of the velocity profiles are
U velocity distributions along the centerline with compressibility modification.
Axial velocity profiles with compressibility modification: (a) X/R=0.079, (b) X/R=1.26, (c) X/R=2.67.
TKE profiles with compressibility modification: (a) X/R=0.079, (b) X/R=1.26, (c) X/R=2.67.
in best agreement with the data. As shown in
Fig. 10
, the Craft model produced smaller TKE than the other models at
X
/
R
= 0.079 and 1.26. This is related to the definition of cμ, which is proportional to the inverse of
The only difference between the
k

ε
Craft model and the MLS2 model is the definition of the strainrelated damping
Pressure coefficient distributions on the base with compressibility modification.
coefficients in
c_{μ}
which causes the observed differences in the TKE distributions. It should be noted that the MLS2 model makes an improvement in the prediction of the TKE profiles. This indicates that it can be considered as an improvement of the
k

ε
LaunderSharma model. However, there are still differences with the experimental data and room for improvements in the model.
Streamwise velocity distribution along the centerline is displayed in
Fig. 11
for the MLS2 model with compressibility modification. The Sarkar and the Wilcox compressibility modifications produce longer reattachment length and larger peak reverse velocity. These models introduce additional amounts of the TKE dissipation in the k equations, which is proportional to the square of the turbulent Mach number. The addition of the dissipation results in the reduction of the turbulent mixing and the increase in the reverse velocity. The simplified Ristorcelli model gives more accurate velocity distribution in the recirculating region and shows that the Ristorcelli model is superior to the other models with regards to the present nonequilibrium flow. The advantage of the Ristorcelli model is also shown in
Fig. 12
for the wall pressure distribution. All modifications increase the pressure coefficient. The Sarkar and Wilcox models produce somewhat larger variations of the pressure distribution than the Ristorcelli model. The mean magnitude of the wall pressure of the Ristorcelli is in excellent agreement with the experimental data.
Velocity profiles, TKE, and primary Reynolds stress profiles with compressibility modification are examined in
Figs. 13

15
for the MLS2 model. A more noticeable effect of these corrections can be observed in the TKE and the primary Reynolds stress profiles. As discussed earlier, the compressibility modification decreases the production of TKE and the Reynolds stress, particularly at
X
/
R
= 2.67. The Sarkar and Wilcox models give better TKE profiles than the Ristorcelli, whereas the velocity and the primary Reynolds stress distributions of the Ristorcelli model are in best agreement with the experimental data.
5. Conclusions
The performance of the
k

ε
models was examined for
Primary Reynolds stress profiles with compressibility modification: (a) X/R=0.079, (b) X/R=1.26, (c) X/R=2.67.
supersonic base flow and two types of modification to the
k

ε
LaunderSharma turbulence model were proposed. The improvements were based on developing the suitable realizability constraint for the LaunderSharma
k

ε
model. It was observed that while the
c_{μ}
function in the Craft model satisfies the realizability condition, the best results (particularly for TKE) are obtained when the cμ function is modified to be a gridindependent function (the MLS2 model). The compressibility modifications to the turbulence equations were also examined for the present supersonic flow. It was shown that compressibility modifications adversely impact the prediction of velocity profiles, but the best agreement with the data was obtained when a simplified Ristorcelli modification was incorporated along with the MLS2 model. Based on the predictions obtained, it was concluded that the MLS2 model proposed here along with the compressibility modifications provides the best agreement to the experimental data in regards to the turbulence quantities.
Acknowledgements
This research was supported by the NSL (National Space Lab) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (No:20110020837).
Herrin J. L.
,
Dutton J. C.
1994
“Supersonic Base Flow Experiments in the Near Wake of a Cylindrical Afterbody”
AIAA Journal
32
(1)
77 
83
DOI : 10.2514/3.11953
Sahu J.
1994
“Numerical Computations of Supersonic Base Flow with Special Emphasis on Turbulence Modeling”
AIAA Journal
32
(7)
1547 
1549
DOI : 10.2514/3.48296
Krishnamurty V. S.
,
Shyy W.
1997
“Study of Compressibility Modifications to the kε Turbulence Model”
Physics of Fluids
9
(9)
2769 
2788
DOI : 10.1063/1.869468
Sarkar S.
1992
“The PressureDilatation Correlation in Compressible Flows”
Physics of Fluids A
4
2674 
2682
DOI : 10.1063/1.858454
Wilcox D. C.
1992
“DilatationDissipation Corrections for Advanced Turbulence Models”
AIAA Journal
30
(11)
2639 
2646
DOI : 10.2514/3.11279
Ristorcelli J. R.
1997
“A PseudoSound Constitutive Relationship for the Dilatational Covariances in Compressible Turbulence”
Journal of Fluid Mechanics
347
37 
70
DOI : 10.1017/S0022112097006083
Bradshaw P.
,
Ferriss D. H.
,
Atwell N. P.
1967
“Calculation of BoundaryLayer Development Using the Turbulent Energy Equation”
Journal of Fluid Mechanics
28
(3)
593 
616
DOI : 10.1017/S0022112067002319
Coakley T. J.
1983
“Turbulence Modeling Methods for the Compressible NavierStokes Equations”
16th AIAA Fluid and Plasma Dynamics Conference
Danvers, MA
June
AIAA Paper 831693
Thivet F.
2002
“Lessons Learned from RANS Simulations of ShockWave/BoundaryLayer Interactions”
40th AIAA Aerospace Sciences meeting & Exhibit
Reno, NV
AIAA Paper 20020583
Park S. H.
,
Kwon J. H.
2004
“Implementation of kω Turbulence Models in an Implicit Multigrid Method”
AIAA Journal
42
(7)
1348 
1357
DOI : 10.2514/1.2461
Menter F. R.
1994
“TwoEquation EddyViscosity Turbulence Models for Engineering Applications”
AIAA Journal
32
(8)
1598 
1605
DOI : 10.2514/3.12149
Launder B. E.
,
Sharma B. I.
1974
“Application of the Energy Dissipation Model of Turbulence to the Calculation of Flows near a Spinning Disk”
Letters in Heat and Mass Transfer
1
131 
138
Gerolymos G. A.
1990
“Implicit MultipleGrid Solution of the Compressible NavierStokes Equations using kε Turbulence Closure”
AIAA Journal
28
(10)
1707 
1717
DOI : 10.2514/3.10464
Craft T. J.
,
Launder B. E.
,
Suga K.
1996
“Development and Application of a Cubic EddyViscosity Model of Turbulence”
International Journal of Heat and Fluid Flow
17
108 
115
DOI : 10.1016/0142727X(95)000796
Barakos G.
,
Drikakis D.
2000
“Numerical Simulation of Transonic Buffet Flows using Various Turbulence Closures”
International Journal of Heat and Fluid Flow
21
620 
626
DOI : 10.1016/S0142727X(00)000539
Wilcox D. C.
1988
“Reassessment of the ScaleDetermining Equation for Advanced Turbulence Models”
AIAA Journal
26
(11)
1299 
1310
DOI : 10.2514/3.10041
Thivet F.
,
Knight D. D.
,
Zheltovodov A. A.
,
Maksimov A. I.
2001
“Insights in Turbulence Modeling for CrossingShockWave/BoundaryLayer Interactions”
AIAA Journal
39
(6)
985 
995
DOI : 10.2514/2.1417
Anderson W. K.
,
Tomas J. L.
,
Van Leer B.
1986
“Comparison of Finite Volume Flux Vector Splittings for the Euler Equations”
AIAA Journal
24
(9)
1453 
1460
DOI : 10.2514/3.9465
Liu F.
,
Zheng X.
1996
“A Strongly Coupled TimeMarching Method for Solving the NavierStokes and kω Turbulence Model Equations with Multigrid”
Journal of Computational Physics
128
(2)
289 
300
DOI : 10.1006/jcph.1996.0211
Wilcox D. C.
1998
Turbulence Modeling for CFD
2nd edition
DCW Industries
La Canada, CA