ArticlePDF Available

Ten years of industrial experience with the SST turbulence model

Authors:

Abstract and Figures

This document describes the current formulation of the SST turbulence models, as well as a number of model enhancements. The model enhancements cover a modified near wall treatment of the equations, which allows for a more flexible grid generation process and a zonal DES formulation, which reduces the problem of grid induced separation for industrial flow simulations. Results for a complete aircraft configuration with and without engine nacelles will be shown.
Content may be subject to copyright.
Turbulence, Heat and Mass Transfer 4
 !"$#%&%'(*),+.-/10(*)32
©2003 Begell House, Inc.
Ten Years of Industrial Experience with the SST
Turbulence Model
F. R. Menter1, M. Kuntz1 and R. Langtry1
1Software Development Department, ANSYS – CFX, 83714 Otterfing, Germany
florian.menter@ansys.com; martin.kuntz@ansys.com, robin.langtry@ansys.com
Abstract This document describes the current formulation of the SST turbulence models, as well as a
number of model enhancements. The model enhancements cover a modified near wall treatment of the
equations, which allows for a more flexible grid generation process and a zonal DES formulation, which
reduces the problem of grid induced separation for industrial flow simulations. Results for a complete aircraft
configuration with and without engine nacelles will be shown.
1. Introduction
The starting point for the development of the SST [1,2] model was the need for the accurate
prediction of aeronautics flows with strong adverse pressure gradients and separation. Over
decades, the available turbulence models had consistently failed to compute these flows. In
particular, the otherwise popular k-ε [3] model was not able to capture the proper behaviour
of turbulent boundary layers up to separation [4]. The Johnson-King model [5] was the first
formulation, which allowed the accurate prediction of separated airfoil flows. Unfortunately,
the model was not easily extensible to modern three-dimensional Navier-Stokes codes due to
its algebraic formulation.
The k-ω model is substantially more accurate than k-ε in the near wall layers, and has
therefore been successful for flows with moderate adverse pressure gradients, but failes for
flows with pressure induced separation [1]. In addition the ω-equation shows a strong
sensitivity to the values of ω in the freestream outside the boundary layer [6]. The freestream
sensitivity has largely prevented the ω-equation from replacing the ε-equation as the standard
scale-equation in turbulence modelling, despite its superior performance in the near wall
region. This was one of the main motivations for the development of the zonal BSL and SST
models.
The zonal formulation is based on blending functions, which ensure a proper selection of
the k-ω and k-ε zones without user interaction. The main additional complexity in the model
formulation compared to standard models lies in the necessity to compute the distance from
the wall, which is required in the blending functions. This is achieved by the solution of a
Poisson equation and is therefore compatible with modern CFD codes.
The SST model was originally used for aeronautics applications, but has since made its way
into most industrial, commercial and many research codes. This is in agreement with the
present authors experience that the need for accurate computations of flows with pressure-
induced separation goes far beyond aerodynamics. The SST model has greatly benefited from
the strength of the underlying turbulence models. In particular, the accurate and robust near
wall formulation of the Wilcox model has substantially contributed to its industrial
Turbulence, Heat and Mass Transfer 4
usefulness. As well, all the model additions developed by Wilcox for rough walls and surface
mass injection etc. can be used with minor modifications [7].
While the original model formulation has largely stayed unchanged from the formulation
given in [1] (small modifications see bellow), there have been several areas of improvement
carried out within the CFX codes. Robustness opimisation have brought the model to the
same level of convergence as the standard k-ε model with wall functions. An improved near-
wall formulation has reduced the near wall grid resolution requirements, which has resulted
in a substantial improvement for industrial heat transfer predictions [8]. Finally, the zonal
formulation of the model has been beneficial in the formulation of an industrial Detached
Eddy Simulation (DES) model. A large number of model validation studies and applications
can be found on the internet.
2. SST Model Formulation
In this section, the complete formulation of the SST model is given, with the limited number
of modifications highlighted.
(
)
(
)
( )
+
+=
+
i
tk
i
k
i
i
x
k
x
kP
x
k
4
t
k
µσ
*
~ (1)
(
)
(
)
( )
( )
ii
w
i
t
ii
i
x
x
k
F
x
x
S
x
U
t
+
+
+=
+
1
12 21
22
µσβαρ
ωρ
ω
Where the blending function F1 is defined by:
=
4
2
2
2*
1
4
500
maxmintanh yCD
k
,
y
,
5
k
F
k
(2)
with
=10
210,
1
2max
ii
kw xx
k
CD
ω
ω
ρσ
ω
and y is the distance to the nearest wall.
F1 is equal to zero away from the surface (k-
ε
model), and switches over to one inside the
boundary layer (k-
ω
model).
The turbulent eddy viscosity is defined as follows:
( )
21
1,max FSa
ka
t
ω
ν
= (3)
Where S is the invariant measure of the strain rate and F2 is a second blending function
defined by:
=
2
2*
25002
maxtanh y
,
6
k
F (4)
A production limiter is used in the SST model to prevent the build-up of turbulence in
stagnation regions:
(
)
ωρβµ
kPP
x
U
x
U
x
U
Pkk
i
j
j
i
j
i
tk *
10,min
~=
+
= (5)
F. R. Menter et al.
All constants are computed by a blend from the corresponding constants of the k-
ε
and the
k-
ω
model via
(
)
FF
+
=
1
21
α
α
α
etc. The constants for this model are: β*=0.09, α1=5/9,
β1=3/40, σk1=0.85, σω1=0.5, α2=0.44, β2=0.0828, σk2=1, σω2=0.856.
The only modifications from the original formulation are the use of the strain rate, S,
instead of the vorticity in Equation 3 and the use of the factor 10 in the production limiter,
instead of 20 as proposed in [1,2].
3. Near Wall Treatment
One of the essential features of a useful industrial turbulence model is an accurate and
robust near wall treatment. In addition, the solutions should be largely insensitive to the near
wall grid resolution. For complex industrial flows the requirement 2<
+
y is excessive and
can in most cases not be satisfied for all walls. On the other hand, the strict use of wall
functions, which allow the use of coarser grids, limits the model accuracy on fine grids. A
new near wall treatment was therefore developed [8], which automatically shifts from the
standard low-Re formulation to wall functions, based on the grid spacing of the near-wall
cell.
Figure 1 shows velocity profiles for Couette flow simulations on three vastly different grids
( 100~;9~;2.0~ +++ yyy ). Despite the large differences in the near wall spacing, the
computed wall shear-stress varies by less than 2% and all solutions follow the logarithmic
profile. As a result, the new wall formulation has significantly improved the predictive
accuracy of general industrial applications, as the user influence via the grid generation is
drastically reduced.
Figure 1 Velocity profiles for three different grids using the automatic wall treatment of CFX-5
4. Application of the SST Model to Aerodynamic Flows
The SST model was selected by CFX for its contribution to the testcases of the 2nd AIAA
drag prediction workshop (http://aaac.larc.nasa.gov/tsab/cfdlarc/aiaa-dpw/). Two geometries
have been selected by AIAA and the grids have been provided by the organizers. Figure 2
shows the geometries simulated by the participants.
Turbulence, Heat and Mass Transfer 4
Figure 2 Geometries selected for AIAA drag prediction workshop
The low-Re grids had 5.83m (WB) and 8.43m (WBNP) hexahedral cells and have been
provided by ICEM. Convergence for the drag (most sensitive variable) has been achieved
typically with around 120-150 time steps.
Figure 3 shows the drag polar for the mandatory runs against the experimental data, as well
as the convergence history. The simulated results are in very good agreement with the
experimental data.
Figure 3 Drag polar for AIAA drag prediction testcases (left). Lift and Drag convergence (right)
This is a strong indication that optimized RANS models/codes can accurately simulate
complete aircraft configurations. More information can be found on the web-page of the
workshop and follow-up AIAA publications.
5. Zonal SST-DES Formulation
Recently, Spalart [9] has proposed a hybrid model formulation that utilises the RANS
equations inside the boundary layer and an LES-like formulation for free shear flows. The
model is termed Detached Eddy Simulation (DES) and is currently used in combination with
the Spalart-Allmaras and the SST turbulence model [10]. The main reason why these models
have been selected as the underlying RANS models lies in their improved separation
prediction capability. The DES modification in the SST model is applied to the dissipation
term in the k-equation as follows:
WB
WBNP
F. R. Menter et al.
== 1,max
**
DES
t
DESDES C
L
FwithFkk
ωρβωρβρε
(9)
where ε is the dissipation rate, is the maximum local grid spacing (
(
)
zyx
=
,,max in
case of a Cartesian grid), β* is a constant of the SST model,
ωβ
*
k
Lt= is the turbulent length
scale and CDES= 0.61 is a calibration constant of the DES formulation.
For fine grids, the switch from RANS to DES can take place somewhere inside the
boundary layer and produce a premature (grid-induced) separation [11]. Figure 4 shows the
effect for a 2-dimensional airfoil simulation. In this case the grid spacing in the spanwise
direction is assumed to be of the same order as the chordwise spacing (this is usually the case
for unstructured meshes or for flows where the flow direction is unknown during the grid
generation phase). It can be seen that the original DES limiter affects the RANS model and
moves the separation point upstream relative to the original SST model, which was in good
agreement with the data (upper right picture).
Figure 4 Region of flow separation on airfoil for different models. Lower right refined grid. Separation
indicated by arrow.
In order to reduce the grid influence of the DES-limiter on the RANS part of the boundary
layer, the SST model offers the option to “protect” the boundary layer from the limiter. This
is achieved again with the help of the zonal formulation underlying the SST model [11]. The
following modification significantly reduces the influence of the DES limiter on the boundary
layer portion of the flow:
( )
21,,0;1,1max FFFwithF
C
L
FSSTSST
DES
t
CFXDES =
=
(10)
SST
-
RANS
SST
-
DES Strelets
SST
-
DES
-
CFX
SST
-
DES
-
CFX
Turbulence, Heat and Mass Transfer 4
In this equation, FSST can be selected from the blending functions of the SST model. For
FSST =0, the model of Strelets [10] is recovered. Figure 4 shows also the effect for the same
2D airfoil, using FSST =F2 . It can be seen that with this modification, the boundary layer is not
affected and the separation point predicted with the SST model is unchanged, even under
more severe grid refinement (lower right picture).
Note that the zonal DES formulation does not completely eliminate the problem of grid
sensitivity in the RANS region, as the F2 function does not cover 100% of the boundary
layer. It does however reduce the critical limit by one order of magnitude.
Another interesting effect of the zonal DES formulation can be seen in Figure 5 for the
flow around a cube mounted inside a 2D channel. At the inlet, a fully developed channel flow
enters the computational domain. For this type of flow, the maximum grid spacing is smaller
than the turbulent length-scale over most of the domain. For the original SST-DES model,
this would mean that the DES limiter is activated over most of the domain, which would
essentially require a simulation carried out in LES modus. For the zonal SST-DES model, the
inlet part is covered by the F2 limiter and can be treated by the RANS model. The DES
limiter is only activated downstream of the cube, where the large turbulent structures are
resolved.
Figure 5 Flow around cube in channel flow. Solution SST-DES-CFX model.
Figure 5 shows the flowfield using iso-surfaces of the invariant
)
)
ijji xUxU
//
coloured by the ratio
µ
µ
/
t. The flowfield upstream is covered by the SST model and is
close to steady-state (except for pressure disturbances from the separated zone) and the flow
downstream is covered by the DES formulation.
Figure 6 Velocity profiles in symmetry plane of cube. Comparison of SST and SST-DES-CFX model with
experimental data.
F. R. Menter et al.
Figure 6 shows a comparison of the velocity profiles computed with the SST and the SST-
DES-CFX zonal model. The main difference is that the DES formulation captures the flow
recovery downstream of the separation zone in good agreement with the experimental data.
6. Future Directions
It has been observed for a long time that RANS turbulence models underpredict the level of
the turbulent stresses in the detached shear layer emanating from the separation line [13].
This in turn seems to be one of the main reasons for the incorrect flow recovery predicted by
the models downstream of reattachment. It was found it the 9th ERCOFTAC/IAHR/COST
Workshop on Refined Turbulence Modelling for the flow over a periodic hill, that models
with improved separation prediction capabilities, like the SST and the SA model did
overpredict the extent of the separated region. This is a matter of concern and is an area of
current research. The problem is shown for the asymmetric diffuser testcase of Obi [14]. The
SST model gives a significantly improved separation compared to the k-ε model, but predicts
a flow recovery that is slower than observed in the experiments. Note that the better
comparison of the k-ε model in this region is an artefact of the underpredicted separation.
Figure 7 Velocity profiles for asymmetric diffuser flow
While an improved flow recovery could be computed with the DES formulation, as shown
in Figure 6 this is not always possible. For pressure induced separation bubbles from smooth
surfaces, the original DES model cannot be applied due to the danger of grid-induced
separation. Alternatively, the zonal DES formulation would stay in RANS mode and would
have no influence on the results. A possible alternative to current DES formulations is the
extension of the Scale-Adaptive Simulation (SAS) approach [15] to the SST model.
Another interesting future development is the combination of the BSL model (underlying
the SST model) with explicit algebraic stress models (EASM) as proposed by Helsten and
Laine [16]. This allows the inclusion of secondary motions and the effects of streamline
curvature and system rotation.
7. Summary
This paper gave an overview of the current state and direction of development of the SST
turbulence model. The standard model formulation has been repeated and extensions for
improved wall treatment and a zonal DES formulation have been presented. Simulations for a
complete aircraft without and with engine nacelle have been briefly discussed. Directions for
future developments have been outlined.
Turbulence, Heat and Mass Transfer 4
Acknowledgement
Part of this work was supported by research grants from the European Union under the FLOMANIA
project (Flow Physics Modelling - An Integrated Approach) is a collaboration between Alenia, Ansys-
CFX, Bombardier, Dassault, EADS-CASA, EADS-Military Aircraft, EDF, NUMECA, DLR, FOI,
IMFT, ONERA, Chalmers University, Imperial College, TU Berlin, UMIST and St. Petersburg State
University. The project is funded by the European Union and administrated by the CEC, Research
Directorate-General, Growth Programme, under Contract No. G4RD-CT2001-00613.
References
1. Menter, F.R. Zonal two-equation k-ω turbulence model for aerodynamic flows. AIAA
Paper 1993-2906, 1993.
2. Menter, F.R., (1994), Two-equation eddy-viscosity turbulence models for engineering
applications. AIAA-Journal, 32(8), pp. 269-289, 1994.
3. Jones, W.P. and Launder, B.E. The prediction of laminarization with a two-equation
model of turbulence. International Journal of Heat and Mass Transfer, 15, 1972.
4. Wilcox, D.C. Turbulence Modeling for CFD. DCW Industries, Inc., La Canada, CA,
1993.
5. Johnson, D.A. and King, L.S. A new turbulence closure model for boundary layer flows
with strong adverse pressure gradients and separation”, AIAA Paper 1984-0175, 1984.
6. Menter, F.R. Influence of freestream values on k-ω turbulence model predictions. AIAA
Journal, Vol. 30, No. 6.1992.
7. Hellsten, A. and Laine, S. Extension of the k-ω-SST turbulence models for flows over
rough surfaces, AIAA Paper 97-3577, 1997.
8. Esch T., Menter, F. R. Heat tranfer prediction based on two-equation turbulence models
with advanced wall treatment, in Proc. Turbulence Heat an Mass Transfer, Eds. Hanjalic,
Nagano, Tummers, Antalya, 2003.
9. Spalart, P.R, Jou, W.-H., Strelets, M. and Allmaras, S.R. Comments on the feasibility of
LES for wings, and on a hybrid RANS/LES approach. 1st AFOSR Int. Conf. On
DNS/LES, Aug.4-8, 1997, Ruston, LA. In Advances in DNS/LES, C. Liu & Z. Liu Eds.,
Greyden Press, Colombus, OH, 1997.
10. Strelets, M. Detached eddy simulation of massively separated flows, AIAA Paper 2001-
0879, 2001.
11. Menter, F.R., Kuntz, M., Adaptation of Eddy-Viscosity Turbulence Models to Unsteady
Separated Flow Behind Vehicles. Proc. Conf. The Aerodynamics of Heavy Vehicles:
Trucks, Busses and Trains, Asilomar, Ca, (to be published by Springer, 2003).
12. Huang, P.G.; Coleman, G.N.; Bradshaw, P. Compressible turbulent channel flow - A
close look using DNS data. AIAA Paper 95-0584* 1995.
13. Johnson, D.A., Menter, F.R., and Rumsey C.L. The status of turbulence modeling for
aerodynamics, AIAA Paper 1994-2226, 1994.
14. Obi, S, Aoki, K and Madsuda, S. Experimental and computational study of turbulent
separating flow in an asymmetric plane diffuser, 9th Symp. on Turbulent Shear Flows,
Kyoto, paper P305, 1993.
15. Menter, F.R. Kuntz, M. and Bender, R. A scale-adaptive simulation model for turbulent
flow predictions. AIAA Paper 2003-0767, 2003.
16. Helsten, A. and Laine, S. Implicit algebraic Reynolds stress modelling in decelerating and
separating flows. AIAA Paper 2000-2313, 2000.
... The SST model has the potential to enhance the accuracy of quantitative predictions for complex turbulent flows, especially those with strong adverse pressure gradients and flow separation typically encountered in axial compressors. Committed to conducting more in-depth research on the SST model, the aim is to enhance its expertise in the simulation of turbomachinery (Menter et al. 2003;Yin et al. 2010). In the subsequent verification, the numerical results obtained using the SST model will also be compared with those from the k-epsilon model and experimental results, demonstrating that the SST model is more suitable for this study. ...
... The turbulence time scale can be estimated based on T = 1/(C µ ω), where C µ = 0.09 and ω is the specific dissipation rate that is obtained by solving the corresponding transport equation. The ω equation can be given by k-ω SST turbulence model [29] ...
Preprint
Learning symbolic turbulence models from indirect observation data is of significant interest as it not only improves the accuracy of posterior prediction but also provides explicit model formulations with good interpretability. However, it typically resorts to gradient-free evolutionary algorithms, which can be relatively inefficient compared to gradient-based approaches, particularly when the Reynolds-averaged Navier-Stokes (RANS) simulations are involved in the training process. In view of this difficulty, we propose a framework that uses neural networks and the associated feature importance analysis to improve the efficiency of symbolic turbulence modeling. In doing so, the gradient-based method can be used to efficiently learn neural network-based representations of Reynolds stress from indirect data, which is further transformed into simplified mathematical expressions with symbolic regression. Moreover, feature importance analysis is introduced to accelerate the convergence of symbolic regression by excluding insignificant input features. The proposed training strategy is tested in the flow in a square duct, where it correctly learns underlying analytic models from indirect velocity data. Further, the method is applied in the flow over the periodic hills, demonstrating that the feature importance analysis can significantly improve the training efficiency and learn symbolic turbulence models with satisfactory generalizability.
... The shear stress transport(SST) k-ω turbulence model was selected due to its accomplished previous studies in the simulation of turbine analysis performance [41][42][43][44]. The SST k−ω turbulence model combines the advantages of the k−ω model in the near-wall region with the k−ε model's ability to handle free-stream turbulence [45,46]. Compared to other turbulence models, such as the standard k−ε, the SST k−ω model offers superior performance in predicting separation and nearwall turbulence, which are crucial for evaluating the efficiency and effectiveness of the turbine under varying flow conditions [47,48]. ...
Article
Full-text available
Hydrokinetic turbines are crucial for sustainable power generation, but their performance is often impacted by floating debris and sediment transport, which can damage turbine blades. Sediment retention enhances the turbine's lifespan and reduces maintenance by preventing blade erosion, cavitation and clogging. Protective grates reduce abrasive particle entry, minimising blade wear. They also avoid buildup of sediment, lowering the risk of blockages and cavitation, which harm efficiency and accelerate degradation. This study presents the numerical performance of Darrieus‐type vertical axis hydrokinetic turbines under the impact of straight and Coanda type grate protection structures. The effects of these two types of grate structures with different design angles on turbine power coefficient (CP) and torque coefficient (CT) were investigated using the ANSYS Fluent program. The dynamic mesh technique simulated the turbine rotation and the semi‐implicit method for pressure‐linked equations (SIMPLE) was applied with a shear stress transport (SST) k‐ω turbulence model. The turbine's efficiency was compared and the results were evaluated for steady and unsteady flow conditions. The highest power coefficients were obtained as 0.230 and 0.264 for steady and unsteady flow, respectively, in the Coanda grate with a 30° central angle. The highest power coefficients were obtained as 0.215 and 0.247 for steady and unsteady flow, respectively, in the straight grate design with a 60° inclination angle. The sediment retention capacities of Coanda grates (30° central angle) and straight grates (60° inclination angle) with varying particle size distributions were further investigated using the discrete phase model (DPM) under steady flow conditions.
... In addition to the IDDES studies, further Unsteady RANS (URANS) studies in line with the 2003 version of Menter's SST model are also conducted, see Menter et al. (2003). Results of both viscous methods largely align, which is why only the IDDES simulations are used as a reference and the URANS simulations only classify the numerical effort at this section's end. ...
Preprint
This paper introduces an inviscid Computational Fluid Dynamics (CFD) approach for the rapid aerodynamic assessment of Flettner rotor systems on ships. The method relies on the Eulerian flow equations, approximated utilizing a state-of-the-art Finite Volume Method with a dynamic momentum source term to enforce rotor circulation. The method offers substantial computational savings by avoiding near-wall refinement and easing time step constraints, making it ideal for early design phases such as design space exploration. Validation against potential flow theory and viscous reference simulations confirms that the method reliably predicts lift-induced forces despite its limitations in capturing parasitic drag. Three-dimensional simulations, including idealized wind tunnel setups and full-scale ship applications at high Reynolds numbers (up to ReL=1E08), demonstrate that results based on low-order convection featuring a solid numerical viscosity yield deviations with respect to viscous reference data of around O(10%). Accepting this reasonable loss of predictive accuracy provides a simulation framework with response times in the order of minutes compared to hours or even days.
... The k-ω SST was used, which yields the most accurate results in terms of RANS models for wall-bounded problems. 37 In addition, all the spatial and temporal terms in Eq. (1) were discretized by the second-order method, and the semi-implicit pressure-linked equation (SIMPLE) algorithm was employed to solve the set of equations. Furthermore, regarding the LES equations, the Wall-Adapting Local Eddy-Viscosity (WALE) model was implemented to capture the subgrid scale (SGS) structures as it is the most suitable modeling technique for the LES computation of channel flows. ...
Article
Full-text available
Superhydrophobic surfaces (SHSs) have garnered significant attention for their potential to reduce hydrodynamic drag under underwater flow conditions. This study investigates the effectiveness of SHSs in reducing drag within Taylor–Couette (TC) flow, a classical fluid dynamics system characterized by the flow between two concentric, rotating cylinders. By employing SHSs on the inner cylinder, we demonstrated a substantial decrease in drag with the implementation of SHSs, particularly in the turbulent regime. To illustrate underlying physics for altered flow behaviors on SHSs, we developed a numerical model based on Navier’s slip model to simulate the rotating SHSs. The simulations were conducted using both medium and high fidelity numerical methods, including unsteady Reynolds-averaged Navier–Stokes and large eddy simulation, which were validated against experimental results in terms of shear stress and velocity profiles. In addition, we employed particle image velocimetry to visualize flow patterns and phenomena in the TC apparatus. The quantitative analysis of flow patterns, vortex formation, and the Reynolds stress tensor was conducted for comparison between smooth surface and flat SHSs. The results showed that the slip velocity decreased the average velocity of the SHS rotor while increasing velocity fluctuations in the TC flow. The heightened velocity fluctuations are due to greater instability from the slip condition, leading to smaller but more numerous vortical structures near the wall. By experimental validation, our numerical model has been proven to understand the role of surface properties in fluid dynamics in the TC flow and may contribute to future advancements in relevant technologies.
Article
Full-text available
For flow-related design optimization problems, computational fluid dynamics (CFD) simulations are commonly used to predict the flow fields. However, the computational expenses of CFD simulations limit the opportunities for design exploration. Motivated by this tricky issue, a convolutional neural network (CNN) based on U-Net architecture with attention mechanism (AM) is proposed to efficiently learn flow representations from CFD results to shorten the compressor blade design cycle. The proposed model converts the provided shape information and flow conditions into grayscale images to directly predict the expected flow field, saving computational time. An extensive hyper-parameter search is performed to determine the optimal model. Qualitative and quantitative analysis of the results are studied to evaluate the accuracy for the calculation of Mach number distributions. In particular, two new attention mechanisms is developed to preserve the physical consistency of the complex flow field with shock wave. Mach number flow fields under different working conditions are predicted using the proposed model, and the prediction is well consistent with CFD results. Over three orders of magnitude of speedup is achieved at all batch sizes compared to traditional CFD methods, while maintaining low prediction errors.
Article
Learning symbolic turbulence models from indirect observation data is of significant interest as it not only improves the accuracy of posterior prediction but also provides explicit model formulations with good in-terpretability. However, it typically resorts to gradient-free evolutionary algorithms, which can be relatively inefficient compared to gradient-based approaches, particularly when the Reynolds-averaged Navier-Stokes (RANS) simulations are involved in the training process. In view of this difficulty, we propose a framework that uses neural networks and the associated feature importance analysis to improve the efficiency of symbolic turbulence modeling. In doing so, the gradient-based method can be used to efficiently learn neural network-based representations of Reynolds stress from indirect data, which is further transformed into simplified mathematical expressions with symbolic regression. Moreover, feature importance analysis is introduced to accelerate the convergence of symbolic regression by excluding insignificant input features. The proposed training strategy is tested in the flow in a square duct, where it correctly learns underlying analytic models from indirect velocity data. Further, the method is applied in the flow over the periodic hills, demonstrating that the feature importance analysis can significantly improve the training efficiency and learn symbolic turbulence models with satisfactory generalizability.
Conference Paper
Full-text available
Finite-volume calculations of a second-moment closure model and a k-epsilon model have been conducted for turbulent flow in an asymmetric plane diffuser. The comparison with the experiment by an LDV demonstrates a superior performance of the second-moment closure over the k-epsilon model.
Conference Paper
Full-text available
The paper is an attempt to provide a comprehensive description of the state-of-the-art in the area of Detached-Eddy Simulation (DES) of massively separated turbulent flows. DES is a new approach to treatment of turbulence aimed at the prediction of separated flows at unlimited Reynolds numbers and at a manageable cost in engineering. It soundly combines fine-tuned Reynolds-Averaged Navier-Stokes (RANS) technology in the attached boundary layers and the power of Large-Eddy Simulation (LES) in the separated regions. It is essentially a three-dimensional unsteady approach using a single turbulence model, which functions as a subgrid-scale model in the regions where the grid density is fine enough for an LES, and as a RANS model in regions where it is not. SGS function or LES mode prevails where the grid spacing in all directions is much smaller than the thickness of the turbulent shear layer. The model senses the grid density and adjusts itself to a lower level of mixing, relative to RANS mode and, as a result, unlocks the large scale instabilities of the flow and lets the energy cascade extend to length scales close to the grid spacing. In other regions (primarily attached boundary layers), the model is in RANS mode. The approach is non-zonal, i.e., there is a single velocity and model field, and no issue of smoothness between regions. The computing-cost outcome is favorable enough that challenging separated flows at high Reynolds numbers can be treated quite successfully on the latest personal computers. We present a motivation for and detailed formulation of the DES approach based on both its original version employing the one-equation Spalart-Allmaras (S-A) turbulence model, and a new one, using the k-w Shear Stress Transport model of Menter (M-SST). Numerical issues in DES are also addressed in terms of both accuracy and efficiency. The credibility of the approach is supported by a set of numerical examples of its application: NACA 0012 airfoil at high (up to 90°) angles of attack, circular cylinder with, laminar and turbulent separation, backward-facing step, triangular cylinder in a plane channel, raised airport runway, and a model of the landing gear truck. The DES predictions are compared with experimental data and with RANS solutions. © 2001 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
Chapter
Turbulence model development for aerodynamic applications has for many years concentrated on improving the capabilities of CFD methods for separation prediction. Validation studies of turbulence models in the ‘80th have clearly shown that most turbulence models were not capable of predicting the development of turbulent boundary layers under adverse pressure gradient conditions. Based on that observation, new models were developed to specifically meet this challenge, resulting in a series of models capable of capturing boundary layer separation in good agreement with experimental data (Johnson and King 1984, Menter 1993, Spalart and Allmaras 1994).
Conference Paper
A recently introduced fully explicit and self-consistent algebraic Reynolds-stress modelling approach, developed by Wallin and Johansson, is combined with k -ω and k -ε two-equation turbulence models. The obtained nonlinear two-equation turbulence models are tested in various two-dimensional decelerating flows with adverse pressure-gradient and How separation as the dominating features. The aim is to compare the predictive realism of linear and nonlinear stress-strain relationships in decelerating adverse pressure-gradient flows, and to study the effect of some modelling choices, e.g., whether to use ε or ω as a length-scale variable, and whether to employ linear or more general quasi-linear pressure-strain modelling. The behaviour of the models and model versions considered is first studied in an axisymmetric separating boundary layer with positive pressure gradient. Next, separating flows in a diffuser, and flows past two aerofoils at a high angle of attack are studied. © 2000 by the A. Hellsten and S. Laine Published by the American Institute of Aeronautics and Astronautics, Inc.