Automotive CFD Prediction Workshop

2nd Automotive CFD Prediction Workshop

Olivier Thiry, Daniel Ocaña Blanco

August 26th, 2021

Auto-CFD 2

© 2021 Cadence Design Systems, Inc. All rights reserved.2

IntroductionAbout Cadence - Numeca

• Numeca is part of Cadence Design Systems (and has been so since February 2021)

• 28 years of experience (Founded by Prof. Charles Hirsch in 1993)

• Worldwide presence with 150+ and now part of a 9000+ employees' group

• Best Exporter Award 2013 & Award for the most innovative company in 2001 and 2013

• Expertise in the simulation of fluid flows, heat transfers and Multiphysics

• Sponsoring the future. In the development of state-of-the-art technologies, Cadence works in partnership on several R&D projects with many universities worldwide.

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https://www.numeca.com/home

https://www.linkedin.com/showcase/cadence-computational-fluid-dynamics/


About Cadence - Numeca

Introduction

INTELLIGENCE

SYSTEMS

IP / SoCs

Numeca OMNIS™

Leader in CFD, multi-physicssimulation and optimization

Fluids and

Thermal

(CFD)

Simulation

and

Analysis

Electro-

magnetic

Thermal

(Electronics)

Electro-

mechanicalenablement

Cadence Design SystemsLeader in Intelligent Systems


• Integrated, parallel & Multiphysics environment for end-to-end

simulations (1D, CAD, CFD, CAA, FEA, MDO)

• Automatic CAD clean-up and high-quality meshing tool

• Solver agnostic mesher with full layer coverage + high level of

compatibility with solvers and in-house / research codes

• HPC / distributed memory + CPU and GPU friendly

• Wide scope of automotive applications

• Today’s focus on External Aerodynamicso Coupled pressure-based solver

o Steady RANS kw-SST with extended wall functions

o Transient DDES-SST* scale resolving (implicit 2nd order)

OMNISTM

Introduction

*Gritskevich, M.S., Garbaruk, A.V., Schütze, J., Menter, F.R. (2012) “Development of DDES and IDDESformulations for thek-ω shear stress transport model”, Flow, Turbulence and Combustion, vol. 88, pp. 431-449.

TM


Case 1: Windsor Body


Case 1: Windsor Body

Variant Mesh size [M]Mesh size

(committee) [M]Max

skewnessMax expansion

ratio

nW (WMRANS) 6.79 6.69 0.818 8.85

nW (WRRANS) 11.03 11.26 0.848 8.84

wW (WMRANS) 7.72 7.74 0.924 10.99

wW (WRRANS) 14.43 14.92 0.942 12.47

Variant Mesh size [M]Mesh size

(committee) [M]Max

skewnessMax expansion

ratio

nW (WRER) 46.98 46.76 0.847 8.83

wW (WRER) 50.20 50.08 0.943 11.92

• Mesh generated in OMNISTM/Hexpress following workshop guidelines.

• Two configurations considered:o No wheels (nW)

o With wheels (wW).

• Three meshes generated for each configurations:

o High Reynolds Wall Modeled RANS mesh (WMRANS)

o Low Reynolds Wall Resolved RANS mesh (WRRANS)

o Low Re Eddy Resolved mesh (WRER).

• Full hexahedral meshes generated with excellent quality

• Commitee mesh sizes given for reference

RANS grids

DES grids

Mesh through a centerline slice

nW wW

Meshes used


• No clear gain in using low Reynolds meshes with the RANS approach.

• DES better captures drag in absolute value, and the variant inter-ranking for both lift and drag.

Case 1: Windsor BodyForces

Reference: Varney, Max. "Base drag reduction for squareback road vehicles." PhD diss., Loughborough University, 2019.

Drag coefficient

Lift coefficient

Delta drag coeff. (wW vs. nW)

Delta lift coeff. (wW vs. nW)

DataCD

(nW)

CD

(wW)

ΔCD

(wW-nW) [counts]

CL

(nW)

CL

(wW)

ΔCL

(wW-nW) [counts]

Varney 2019 (Exp) 0.280 0.372 92 -0.0271 0.1372 164.3

WMRANS 0.2699 0.3461 76.2 -0.1176 0.1233 240.9

WRRANS 0.2740 0.3400 66.0 -0.1099 0.1033 213.2

WRER (DDES) 0.2819 0.3802 98.3 -0.1210 0.0588 179.8


Case 1: Windsor BodyPressure coefficient on Y and Z cuts

No

wh

ee

lsW

ith

wh

ee

ls

Z=259.4mm

Y=0.0mm

Experiment(Varney 2019)WMRANSWRRANSWRER (DDES)


Case 1: Windsor BodyPressure coefficient on the backface on Y cuts

Plane 1

No

wh

ee

lsW

ith

wh

ee

ls

Plane 2 Plane 3 Plane 42 43

1

Experiment(Varney 2019)WMRANSWRRANSWRER (DDES)


Case 1: Windsor BodyWake pattern comparison: streamwise velocity

WMRANS (6,8M)Experiment (Varney 2019) WRRANS(11M) WRER (47M)

No w

hee

lsW

ith

wh

ee

ls

Isosurface of Q-criterion=1e5 in the

DDES-SST nW simulation (WRER)

Y=0.0mm


DDES-SST wW simulation (WRER)


Case 1: Windsor BodyVelocity profiles in the wake

ux Ux,rms

Uz,rms

ux

Streamwise velocity: Exp., WRRANS, WRER (DDES) Velocity fluctuations: Exp., WRER (DDES)

No

wh

ee

lsW

ith

wh

ee

ls

Y=0.0mmY=0.0mmY=0.0mmZ=194mm


Case 2: DrivAer Notchback


Case 2: DrivAer NotchbackMeshes used

• Mesh generated in OMNISTM/Hexpress following workshop guidelines.

• One configurations considered:o Notchback version of the open cooling DrivAer with detailed underbody

• Two meshes generated :o High Reynolds Wall Modeled mesh (WM)

o Low Reynolds Wall Resolved mesh (WR)

• Full hexahedral meshes generated with excellent quality

• Committee mesh sizes given for reference

VariantMesh size

[M]Mesh size

(committee) [M]Max skewness

Max expansion ratio

Notchback (WM)

136 128 0.935 45.29

Notchback (WR)

236 244 0.929 82.34

View of the mesh in the center plane


Case 2: DrivAer NotchbackAerodynamic Forces

• The experiment from FKFS, with a smallerblockage ratio, is more in line with the simulations.

• No clear gain in using low Reynolds meshes withthe RANS approach.

• DES improves the prediction of drag and rear lift.

Ecara website: https://www.ecara.org/fileadmin/media/dateien/DrivAer/FKFS/FKFS_OCDA_MOD-B_N_EB_wM_wW_woL_oG_KBS.pdf

Hupertz, Burkhard, Chalupa, Krueger, Howard, Glueck, Lewington, Chang, and Shin. “On the Aerodynamics of the Notchback Open Cooling DrivAer: A

Detailed Investigation of Wind Tunnel Data for Improved Correlation and Reference”. SAE Technical Paper, 2021.

CD CLF CLR

FKFS reference(Exp) 0.275 0.015 0.127

RANS-SST (WM) 0.270 -0.068 0.179

RANS-SST (WR) 0.268 -0.060 0.184

DDES-SST (WR) 0.277 -0.098 0.122

Rear Lift coefficient Front Lift coefficient

Drag coefficient

https://www.ecara.org/fileadmin/media/dateien/DrivAer/FKFS/FKFS_OCDA_MOD-B_N_EB_wM_wW_woL_oG_KBS.pdf


Case 2: DrivAer NotchbackPressure coefficient at y=0

• On the front part of the car, no significant difference is observed, as RANS and DES exhibit the same turbulence modeling behavior.

• On the rear section and under the car, the DES model acts as an LES and is closer to the experiment.

Isosurface of Q-criterion=5e4 in

the DDES-SST simulation

Y=0.0mm

Exp. (Hupertzet al. 2021) ; RANS-SST (WM) ; RANS-SST (WR) ; DDES-SST (WR)


Pressure coefficient in horizontal cuts

• DES shows a clear advantage in predicting the wake pattern on the rear section

Z=500.0mmZ=150.0mm

Exp. (Hupertzet al. 2021) ; RANS-SST (WM) ; RANS-SST (WR) ; DDES-SST (WR)



Total pressure coefficient in the wake (x=4m)

• Main body wake better predicted by the DES, but also the wheels wake close to the ground, as well as the underbody wake.

Exp (Hupertz et al. 2021) RANS - SST (WR) DDES - SST (WR)

Isosurface of Q-criterion=5e4 in

the DDES-SST simulation

Ad

im. v

elo

city

Tota

l pre

ssur

e co

eff.

RANS - SST (WM)

X=4000.0mm



Flow in the front wheel’s wake (x=0.4m)

Exp (Hupertz et al. 2021) RANS - SST (WM) RANS - SST (WR) DDES - SST (WR)


DDES-SST simulation

X=400.0mm

Adi

m. v

elo

city

Tota

l pre

ssur

e co

eff.



Streamwise velocity and pressure under the car (z=-0.2376)

Exp. (Hupertz et al. 2021)

RANS - SST (WR)

DDES - SST (WR)

Isosurface of Q-criterion=5e4 in the DDES-SST simulation

Adim. velocity Total pressure coeff.

Exp RANS (WR) DES (WR)

CD 0.275 0.268 0.277

CLR 0.127 0.184 0.122

RANS - SST (WM)

Z=-237.6mm



Conclusions


• Overall good drag trends prediction using RANS for the Windsor Body and DrivAer Notchback

• No influence of mesh refinement on RANS cases past a certain level

• High level of accuracy reached with DDES (preferred approach) on both lift and drag

• Systematic capturing of flow physics in agreement with measurement around wheels, underbody and wake regions

• Robust end-to-end workflow fully automated with embedded best practices

Conclusions

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Automotive CFD Prediction Workshop

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Transcript of Automotive CFD Prediction Workshop