DES and Adaptive-Mesh RANS Simulations for the SAE Notchback...

1st Automotive CFD Prediction Workshop, Oxford, UK

DES and Adaptive-Mesh RANS Simulations for the SAE Notchback Case using OpenFOAM®M. [email protected]. [email protected]. [email protected]

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Company Introduction

11.12.2019 1st Automotive CFD Prediction Workshop, Oxford, UK

Upstream CFD GmbH

• Founded in Berlin in January 2019• Team of five co-founders:

– Charles Mockett (MD), Marian Fuchs, Felix Kramer, Thilo Knacke & Norbert Schönwald– Established team with a total of 60 years professional experience

• Areas of expertise:– Turbulence modelling– Aeroacoustics– Numerical methods– Optimisation– High-performance computing

• Services offered:– R&D: Improved CFD/CAA methods– Automated & adaptive CFD/CAA processes for specific applications– Aerodynamic and aeroacoustic consulting based on high-fidelity simulations– HPC system support

1st Automotive CFD Prediction Workshop, Oxford, UK11.12.2019 3

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Remark on Experimental Data


Remark on experimental data

• There seem to be some issues with symmetry in the experimental data– Perhaps a slight yaw angle offset?

• The “evidence”:– Slight negative side force coefficient at ! = 0°– Asymmetric lift and drag trends with !– Negative %& values at centreline in PIV data

• Perhaps the measured drag coefficient is slightly higher than it would be in a true symmetric flow

11.12.2019 1st Automotive CFD Prediction Workshop, Oxford, UK 5

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DES Results


DES methodology

• Turbulence models compared:– Standard SA-DDES– Grey-area improved SA-!-DDES

• Accelerated RANS-LES transition in free shear layers

• Tested for a wide range of flows: So far always an improvement w.r.t. std. DDES

• OpenFOAM-v1906• DES committee grid

– ANSA, hex-dominant– 29.1M cells, 23 near-wall prism layers

• Numerics:– 2nd order in space & time– Robust, low-dissipation convection scheme

for DES• Unsteady parameters:

– ∆# = 1.0×10)*+– Initial transient: 0.15+à ~ 7 CTUs– Averaging time: 0.2+à ~ 10 CTUs


For more information see e.g.:M. Fuchs et al. (2020) The Grey-Area Improved !-DDES Approach: Formulation Review and Application to Complex Test Cases. Proc. 7th Symposium on Hybrid RANS-LES Methods

Std. DDES !-DDES

Resolved turbulent structures

• Iso-surfaces of Q criterion– Fine-grained turbulence resolution– No spurious “wiggles”


SA-DDES SA-!-DDES

Separation on rear slant

More rapid development of resolved turbulence in vortices / shear layers

Comparison to measurements

• SA-!-DDES drag around 6% above measurement• Std. DDES drag around 4% below measurement• Separation on rear slant not seen in pressure &

PIV measurements


Exp. SA-DDES SA-"-DDES

#$ 0.2071 0.1989-4.0%

0.2191+5.8%

#% 0.0548 -0.0703 -0.0509

PIV Std-DDES !-DDES

Explanation: Shielding (mal)function on roof

• DDES shielding function designed to ensure that attached boundary layers are treated with RANS mode– Otherwise modelled stress depletion (MSD) can cause grid-induced separation (GIS)

• DDES shielding function known to break down on fine grids• The !-DDES function was recalibrated to give equivalent shielding to std. DDES for a flat plate

– In this case, however the !-DDES shielding breakdown is much stronger than for std.-DDES


Boundary layer profiles near rear end of roof

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Adaptive-Mesh RANS Results


Adaptive RANS methodology

• RANS model comparison:– Spalart-Allmaras– Menter SST– Elliptic blending lag (EBL) model (Lardeau & Billard)

• Adaptive Mesh Refinement (AMR):– Generally-applicable in-house sensor formulation

• No case-specific heuristics– Dynamic load balancing– Refinement also possible in prism layers– Fully automated process

• OpenFOAM simulations– Half model with symmetry plane– Same starting grid for all simulations:

• snappyHexMesh, approx. 3M cells• No volume refinement• Same surface Δ" & Δ|| as committee grid

– Final AMR meshes approx. 5.5-6.9M cells (model-dependent)



• All RANS predict attached flow on rear slant• Only minor differences in prediction of rear

slant pressure and vortex formation• Most significant differences in predicted

pressure on rearward-facing base surface– EBL model shows closest agreement to

measured pressure profile• SST and EBL predictions generally similar• High drag from SA model due to shorter wake

recirculation region


Exp. SA-RANS SST-RANS EBL-RANS

Cell count (final) 5.5M 6.9M 6.9M

!" 0.2071 0.234+13%

0.192-7.3%

0.191-7.8%

!# 0.0548 -0.0932 -0.0813 -0.076


• All RANS predict attached flow on rear slant• Only minor differences in prediction of rear

slant pressure and vortex formation• Most significant differences in predicted

pressure on rearward-facing base surface– EBL model shows closest agreement to

measured pressure profile• SST and EBL predictions generally similar• High drag from SA model due to shorter wake

recirculation region


Exp. SA-RANS SST-RANS EBL-RANS

Cell count (final) 5.5M 6.9M 6.9M

!" 0.2071 0.234+13%

0.192-7.3%

0.191-7.8%

!# 0.0548 -0.0932 -0.0813 -0.076

PIV

SA-RANS

SST-RANS

EBL-RANS

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Conclusions and Outlook


Conclusions & outlook

Conclusions• Hybrid numerical scheme for DES minimises dissipation and remains robust• Enhanced DDES model fails due to unexpected shielding function collapse on roof

– Result: erroneous flow separation on rear slant, higher drag• Good test case for near-wall treatment of scale-resolving simulations (spurious

separation if turbulent BL on roof not correctly captured)• Successful demonstration of in-house Adaptive Mesh Refinement process

– Reduced user burden, optimised computational expense, greater fidelity• Std. DDES, SST-RANS and EBL-RANS models predict lower drag than experiment

– Is experimental drag higher due to flow asymmetry issues?– How do other partners’ results compare?

Outlook• Re-run !-DDES model with improved shielding function• Simulation of DrivAer case• Aeroacoustics case in future workshop?


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Thank you for your attention


DES and Adaptive-Mesh RANS Simulations for the SAE Notchback...

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