[PPT] - 1 st Automotive Aerodynamics workshop The University of Sydney Mr PowerPoint Presentation

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1st Automotive Aerodynamics workshop The University of Sydney

Mr Liang Yu

Dr. Asiful Islam (now JLR)
Dr. Sammy Diasinos (Macquarie)

A/Prof Ben Thornber

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Outline

1. Introduction to research group
2. Numerical methods, models and meshes
3. Notchback Results
4. Case 2a
5. Conclusions.

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1. Introduction to Research Group
1. Develop governing models for

compressible turbulent flows

2. Explore novel numerical methods

for unsteady turbulent flows.

3. Unsteady applied aerodynamics

computations (aero and auto)

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1. Prior relevant research – 2013-2016

– Developed hybrid RANS-ILES methods with flow-adaptive blending, incorporating wall functions1 – Targeting Automotive Aeroacoustics (compressible solver) – Smooth transition from Spalart Allmaras to ILES – can use the equation to keep regions RANS or ILES when desired – Utilises an additional scalar transport equation to ‘label’ the boundary layer – simple solution to remove some sensitivity to gridding

1Islam, A. & Thornber, B, Computers and Fluids, 167, 292-312, 2018.

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1. Prior relevant research– 2013-2016

– Undertook hybrid RANS-ILES and Powerflow computations of the notchback in ~2015.

PIV RANS-ILES

Flamenco Powerflow

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1. Prior relevant research– 2013-2016

Powerflow captured experimental pressure drop at rear of the backlight (y=102). Flamenco captured the base flow better

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2. Numerical Methods: Fractional step method for

low-Mach number

1) Momentum predictor to get intermediate velocity 2) Pressure gradient added – similar to Rhie-Chow interpolation 3) Pressure corrector 4) Final velocity update

Transient SIMPLE (PimpleFOAM): (1 -> 2 -> 3 -> 4) -> (1 -> 2 -> 3 -> 4)…(usually 5-8 iterations) until converged per time step Fractional step method: (1 -> 2 -> 3 -> 4) once only per time step

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2. Test Case One: linear gas dynamics

Propagation of a Gaussian pulse of amplitude 0.09Pa (solid lines) Splitting into two waves at late time (dash-dotted lines) Formal convergence study against analytical solution from characteristics Pressure (Pa) Distance (m)

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2. Test Case One: linear gas dynamics

L2 error norm dx (m) Computational time (s) On the same grid, L2 errors are equal For the same grid, the fractional step method is ~5x faster than rhoPimpleFoam Solid lines: CFL=4 Dashed lines: CFL=2e-3 Fractional step (circles) rhoPimpleFoam (asterix)

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2. Test Case Two: Isentropic vortex convection

Advection of an isentropic vortex with a mean flow:

Aligned with the mesh (0 deg)
Angled to the mesh (45 deg)

Run for both the fractional step method (cFSM) and existing rhoPimpleFoam

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2. Test Case Two: isentropic vortex convection

Dashed lines: 0 deg Dotted lines: 45 Deg Fractional step (squares) rhoPimpleFoam (plus) L2 error norm dx (m) Computational time (s) On the same grid, L2 errors are equal For the same error, the fractional step method is ~5x faster than rhoPimpleFoam Benefit does not diminish when the flow direction is not aligned with the grid

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2. Mesh for Case 1

Refinement zones Stationary ground (non-slip) SAE notchback (non-slip) 𝑽∞

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2. Mesh for Case 1

Total Cells Size on car Prism layer Coarse 6 Million 2.3mm 16 layers, 1st height = 0.0115mm Growth rate 1.313(leg) and 1.360 Medium 9 Million 2.05mm 16 layers, 1st height = 0.01025mm Growth rate 1.313(leg) and 1.360 Fine 12 Million 1.875mm 16 layers, 1st height = 0.009375mm Growth rate 1.313(leg) and 1.360

SAE car body 𝑽∞ Stationary ground

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2. Mesh for Case 1

16 layers 1.36 growth 16 layers 1.313 growth legs SAE body Stationary ground 16 layers 1.36 growth 𝑽∞ 1) First layer y+ ~ 1 for a low-Re mesh. 2) With growth rate of 1.313 and 1.36, the total layer thickness is more than y+ = 250. 3) Final layer thickness is 0.3-0.5 of the adjacent cell size for smooth transition. Final layer Adjacent cell

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2. Mesh for Case 1

legs SAE body Stationary ground 𝑽∞ 1) snappyHexMesh allows mesh distortion to add prism layer 2) Mesh quality needs to be carefully controlled for max prism layer coverage 3) Influence of mesh qualities on flow solution: Face pyramids -> discretisation conservation Non-orthonality -> diffusive term accuracy Skewnesss -> convective term interpolation accuracy Cell determinate -> gradient term accuracy Tet decomposition -> Affects lagrangian computation

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2. Mesh for Case 1

SAE body 𝑽∞ 1) The distance at which distortion is allows is also controlled so that the distortion does not affect all interior cells. 2) The distance is important for prism layer coverage around corners SAE body 𝑽∞ Distortion distance

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2. Mesh for Case 1: y+

Needs much smaller surface mesh here for thicker prism layer coverage. The wall boundary condition of the turbulent viscosity is the continuous nutUSpaldingWallFunction in OpenFOAM.

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2. Mesh for Case 2a

Contact patch Low-Re, 16 layers

n car body

High-Re, 4 layers

n wheels

High-Re, 4 layers on contact patch and moving ground Rear wheel well

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2. Mesh for Case 2a

Number of prism layers reduces at some ‘sharp’ locations. Currently under testing: snappyHexMesh addLayer process modified to force generate one layer on all surfaces

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2. Computation time summary

Case 1 Number of Cells 12 Million Inflow Velocity 40 m/s Number of processors 960 Time Step 1.0e-05 s Simulation Time 2.0s Clock Time 14.7 h Case 2a Number of Cells 80 Million Inflow Velocity 16 m/s Number of processors 960 Time Step 1.0e-04 s Simulation Time 2.0s Clock Time 10 h

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2. Other computational details

Turbulence model: compressible SST-DDES in OpenFOAM-v1612+ Discretisation: Temporal terms: Second-order backward differencing Gradient terms: Gauss linear with a limiter (Minmod) Turbulence quantities: Second-order upwind Diffusive term discretisation: Central differencing with full non-orthogonality correction Momentum convective terms: Hybrid convection scheme of Travin et al. for hybrid RANS/LES calculations Central differencing in LES-like region Second-order upwind elsewhere

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3. Workshop Case 1

Iso-surface of Q = 100000 1/s Second-order upwind Second-order upwind Second-order central differencing

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3. Workshop Case 1

Cd Cl Cm Coarse mesh 0.197

0.076
0.102

Medium mesh 0.194

0.075
0.115

Fine mesh 0.195

0.079
0.114

Experiment 0.207 0.054

0.073
2.5
2
1.5
1
0.5

0.5 1 1.5

0.6
0.4
0.2

0.2 0.4 0.6

Cp [-] X [m]

Cp-centreline over the car

Cp-experiment Cp-coarse mesh Cp-medium Cp-fine

0.7
0.6
0.5
0.4
0.3
0.2
0.1

0.1 0.2 0.3 10 20 30 40 50

Cp [-] Sensor number

Cp-backlight

Cp-experiment Cp-coarse mesh Cp-medium Cp-fine

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3. Workshop Case 1

Time-averaged Cp on the SAE model, left Flamenco (in-house, very high order compressible solver), right OpenFOAM Fractional step.

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4. Workshop Case 2a

Whole car Car no wheels

Cd 0.260 0.185 Cl

0.070
0.073

Clf

0.145
0.151

Clr 0.075 0.078

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4. Workshop Case 2a

Iso-surface of Ux = -0.001 m/s

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4. Aeroacoustics of side mirror

Iso-surface of Q = 100000 1/s Wall shear stress magnitude

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5. Conclusions and Acknowledgements

Gave a quick introduction to our recent developments of a compressible fractional step method – No reduction in accuracy compared to current best

ptions

– Considerable speed saving in practical configurations – Weakly compressible – may be used for direct aeroacoustics computations Preliminary results gained for Case 1 and Case 2a. Grid convergence and further analysis for Case 2a still underway.

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