2nd automotive cfd prediction

2nd Automotive CFD Prediction

WorkshopMarian Zastawny, Peter Altmann, Sylvain Lardeau

Siemens Digital Industries Software

UnrestrictedUnrestricted | © Siemens 2021 | 2021-08-26 | Marian Zastawny | 2nd Automotive CFD Prediction Workshop | Siemens Digital Industries Software | Where today meets tomorrow.

Simcenter STAR-CCM+

Unrestricted | © Siemens 2021 | 2021-08-26 | Marian Zastawny | 2nd Automotive CFD Prediction Workshop | Siemens Digital Industries Software | Where today meets tomorrow.

Tessellated Surface

Wrapped Surface

Remeshed Surface

0%

25%

50%

75%

100%

0

448

896

1,344

1,792

0 448 896 1,344 1,792

SC

ALIN

G

SP

EE

D U

P

CORES

Complex Geometry Handling

Speed and Performance

Accurate Physics

Robust and Precise Meshing

Workflow Automation

Intelligent Design Exploration

Turbulence Modelling


Statistical model (RANS)

SA

1. Standard

2. High-Re

k-e

3. Std.

4. Std. 2-layer

5. Std. low-Re

6. Realizable

7. Real. 2-layer

8. AKN

9. V2-f

10. EB k-e

11. Lag EB k-e

k-w

12. Standard

13. SST

RSM

14. Linear pressure-strain

15. Quad. Pressure-strain

16. EB-RSM

Scale-Resolving

DES

17. SA

18. SST k-w

19. EB k-e

LES

20. Smagorinsky

21. WALE

22. Dynamic Smagorignsky

Cost

Influence of modelling assumptions

Influence of user input

Robustness

Model

High

Low

Why to use SRS in automotive?


Windsor Body RANS


Analysed results


Wall-Modelled RANS


SST, a1 = 1.0 Lag EB

Wall-Resolved RANS


SST, a1 = 1.0 Lag EB

RANS Comparison

• Wall-Modelled RANS appears to be closer to

experiment than Wall-Resolved RANS

• SST (a1=1) predicts higher drag coeffcients

• Wake prediction is somewhat correlated with the

reported forces

• Change in velocity profiles is less substantial than

change in drag coefficient


Windsor Body SRS


Wall-Resolved DDES


DDES Summary


• Mean flow solution is achieved almost

instantaneously

• Wake prediction is visibly better than in

RANS

• Wall-Modelled mesh simulation appears to

agree more with the experiment

• Agreement in wake prediction is correlated

with the agreement in reported forces

Scale-Resolving Hybrid (SRH) Turbulence Model

Model Description

When to Use it?

Typical Applications

• Increased fidelity unsteady RANS type modeling

• Higher accuracy than comparable URANS models

• Enhanced temporal modeling for increased realism

• Enables potential accuracy improvement to existing URANS

simulations

• Minimal additional computational cost

• Minimal work for setting up

• Mid-fidelity unsteady applications

• Applications where URANS previously was used

Increased fidelity URANS

URANS

SRH

Unrestricted | © Siemens 2021 | Version 2021.2 | Siemens Digital Industries Software | Where today meets tomorrow.Page 15

Wall-Modelled SRH


Wall-Resolved SRH


DDES vs SRH


• Impact of changing the mesh is the same for

SRH as for DDES

• SRH profiles are generally in better

agreement with experiment than DDES

• SRH shows less sensitivity with respect to

the numerical set-up

DrivAer


Summary


Forces Comparison


RANS Scale-Resolved

Open questions

• Impact of time-step

• Impact of numerical discretization

• Impact of the tunnel walls on CL and CM

• Sensitivity to Boundary Conditions


ContactMarian Zastawny

Senior Application Engineer200 Shepherds Bush Road

W6 7NL London

United Kingdom

E-mail [email protected]


mailto:[email protected]

2nd automotive cfd prediction

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