1st Automotive Aerodynamics workshopautocfd-transfer.eng.ox.ac.uk/Presentations/029-Sydney... ·...

The University of Sydney

1st Automotive

Aerodynamics workshop


Mr Liang Yu

Dr. Asiful Islam (now JLR)

Dr. Sammy Diasinos (Macquarie)

A/Prof Ben Thornber


Outline

1. Introduction to research group

2. Numerical methods, models and meshes

3. Notchback Results

4. Case 2a

5. Conclusions.


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)


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.


1. Prior relevant research– 2013-2016

– Undertook hybrid RANS-ILES and Powerflow computations of

the notchback in ~2015.

PIV RANS-ILES

Flamenco

Powerflow


1. Prior relevant research– 2013-2016

Powerflow captured experimental pressure drop at rear of the

backlight (y=102).

Flamenco captured the base flow better


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


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

Pre

ssure

(P

a)

Distance (m)


2. Test Case One: linear gas dynamics

L2 e

rror

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)


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


2. Test Case Two: isentropic vortex convection

Dashed lines: 0 deg

Dotted lines: 45 Deg

Fractional step (squares)

rhoPimpleFoam (plus)

L2 e

rror

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


2. Mesh for Case 1

Refinement zones

Stationary ground (non-slip)

SAE notchback (non-slip)

𝑼∞


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


1.360

Fine 12 Million 1.875mm 16 layers, 1st height =

0.009375mm


1.360

SAE car body

𝑼∞

Stationary ground


2. Mesh for Case 1

16 layers

1.36 growth

16 layers

1.313 growthlegs

SAE body

Stationary ground

16 layers

1.36 growth

𝑼∞

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


2. Mesh for Case 1

legs

SAE body

Stationary ground

𝑼∞

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


2. Mesh for Case 1

SAE body

𝑼∞

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 body𝑼∞

Distortion

distance


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.


2. Mesh for Case 2a

Contact

patch

Low-Re, 16 layers

on car body

High-Re, 4 layers

on wheels

High-Re, 4 layers on contact patch

and moving ground

Rear wheel

well


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


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


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 regionSecond-order upwind elsewhere


3. Workshop Case 1

Iso-surface of Q = 100000 1/s

Second-order upwind

Second-order upwind

Second-order central differencing


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

0.5

1

1.5

-0.6 -0.4 -0.2 0 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

0.1

0.2

0.3

0 10 20 30 40 50

Cp [

-]

Sensor number

Cp-backlight

Cp-experiment

Cp-coarse mesh

Cp-medium

Cp-fine


3. Workshop Case 1

Time-averaged Cp on the SAE model, left Flamenco (in-house, very high order

compressible solver), right OpenFOAM Fractional step.


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


4. Workshop Case 2a

Iso-surface of Ux = -0.001 m/s


4. Aeroacoustics of side mirror

Iso-surface of Q =

100000 1/s

Wall shear stress

magnitude


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

options

– 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.


Thank-you for listening

1st Automotive Aerodynamics workshopautocfd-transfer.eng.ox.ac.uk/Presentations/029-Sydney... ·...

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