Unsteady Vehicle Simulation

• Why run unsteady?

– Steady State Advantages

• Quick turn-around time

• Proven accuracy in absolute values, and trends

Excellent tool for optimization studies to reduce drag.

– Proven to provide as accurate results for unsteady simulation, yet has lower hardware constraints, and significantly shorter turn-around (hours instead of days)

– Unsteady Advantages

• Certain drive conditions require unsteady simulation

» Vehicle Handling

» Overtaking

» Fleet Modeling

– Can improve aerodynamics by understanding the unsteady flow characteristics

» Aero-acoustics

» Wheel Well Modeling

» etc

DES Simulation

Unsteady Vehicle Simulation

Recommended Turbulence Model:

SST (Menter) K-Omega Detached Eddy Simulation

– Time: 2nd order temporal solver (default is 1st order)

– For wake flows, DES is more accurate than RANS.

– Some publish studies have shown decent results with Spalart-Almaras.

• In-house studies still show K-Omega SST to be more accurate than Spalart-

Almaras solution.

– URANS does provide benefits when running thermal transient simulations

• Large time periods more critical then local flow structures.

DES Simulation

Biggest Disadvantage of Unsteady Simulation

Turn-around time– Key reason why it would really be difficult getting F1 team to run unsteady

simulation over steady since regulated by CPU hours.

Still, Several factors can help reduce overall turn-around– Time Step

• Using Transient SIMPLE, time step can be increased significantly– This is a blessing and curse

» Allows for significantly faster turn-around time

» Can be raised too high, that solution becomes un-physical.

– Inner Sweeps/Relaxation Factors• Transient simulation can and should be run with higher relaxation factors then the

steady solution. The higher the relaxation factor, the few inner sweeps are needed.

– Grid density• Grid density needs to be refined enough to capture local physics. Over refinement will

cause slow down in performance.

DES Simulation

Initializing The Solution

DES Simulation

• To reduce run-time, transient simulations are

initiated using steady flow field.

– Using K-Omega SST to initialize the flow field helps

reduce jump between steady and DES simulation

– Current example uses couple solver.

– The couple solver has possibility to reduce run-time for

large models.

*The Segregated solver is recommended for

transient simulation

– Faster per iteration

– Converges the time step in fewer inner iterations

Size 60 million cells

Steady Solution, 80 processors

20 hours

DES Simulation, 128 Processors

3 Days

Time Step Size

• Resolving Length Scale Determines both on grid size and time step– First, the grid size needs to be fine enough to capture length scale.

– Second, times step needs to match grid size and flow physics (convection speed)

Target for passenger cars have been to resolve wake of vehicles and tires. Small

length scales have been shown to be less critical. This could change with a large

front spoiler, or vehicles with large ground clearance.

DES Simulation

High Speed Aero Study11 Vehicle Speed 140 kph12 Length Scale 40 mm13 Time Step 0.001 s

Formula: =(C12/1000)/(C11/3.6)

Time Step Size: Checking Results

Convective Courant Number

– Detached regions should have courant number below 1.

Regions on exterior, where flow is accelerating around

corners, values above 1 do not seem to affect solution.

DES SimulationExample shows excessively high current numbers around the passenger and front of the vehicle.

Time Step Influence

8

URANS t=0.001s

URANS t=0.01s

URANS t=0.0002s

Large time step show large wake

fluctuation, and increase drag

Inner Sweeps/Relaxation Factors

Parameter Default Optimum Performance

Practical

Inner Sweeps 20 3 5-8

Velocity Relaxation Factor 0.7 0.9 0.8

Pressure Relaxation Factor 0.3 0.9 0.4

DES Simulation

Relaxation Factors

– The default relaxation factors from the steady simulation can be increased when running unsteady.

How high, really depends per case being examined. It is not typical for detail vehicle aerodynamics

that relaxation factors can be raised above 0.8 for velocity, and 0.4 for pressure.

Inner Sweeps

– Default inner sweeps is 20 which is conservative. Most cases, 8 inner sweeps is significant to

converge time step. Lowering inner sweeps between 3-5 help reduce turnaround time even further.

In-house experience with aerodynamics have shown lowering inner sweeps to

speed up the solution is better than increasing time step. Large time step can

significantly impact results.

Add Pressure Monitors For Inner Sweep

Convergence

• Monitors For Solution:– Force on Vehicle

– Pressure probes in tire/vehicle wakeLook for inner iterations to converge. May need to tighten scale to be able to see inner sweeps. 80% convergence to final value seems to be fine for Drag prediction. What we are seeing the change due to inner iteration convergence consistent direction with time. So even since we are seeing 10-15 inner iterations needed for good convergence, 5-8 may be fine for accurate drag predictions. Going to 10-15 just takes longer to achieve final drag values.

– Velocity MagnitudePeak velocity should be below 150 m/s. If Higher, raising CdesTimeLim can improve stability

DES Simulation

Monitoring The Solution

DES Simulation

Relaxation/Inner Sweep Optimization

DES Simulation

At iteration 440, relaxation of

pressure was raised to 0.7, inner

iterations dropped to 10. Higher

relaxation factors show faster

convergence per time step. Fewer

inner iterations reduce run time by

50%.

Inner Sweeps vs. Change in Time

DES Simulation

Fewer inner sweeps are

used to speed up the time

it takes for the flow field to

fully develop.

For fast turn-around, the goal is to develop the flow as fast as

possible. Too many inner sweeps really just delay the time it

takes to get to fully developed flow field

Grid Resolution: Useful Field Functions

Id Name Function Name Definition Purpose

1 Turbulent Length Scale

TurbulentLengthScale pow(.09,-.25)*sqrt($TurbulentKineticEnergy)/$SpecificDissipationRate

Determines Turbulence Length scale for K-Omega SST model

2 Grid Size GridSize pow($Volume,1.0/3.0) Estimate Edge Length of cell (based upon cubes)

3 Length Ratio LengthRatio $TurbulentLengthScale/(2*$GridSize) To properly resolve turbulence, length ratio > 1 would be needed

DES Simulation

Grid Resolution

– Examining the results from the steady analysis can help determine grid

resolution for DES simulation.

• The set of field functions are used to estimate the turbulent length scale,

based upon the turbulent dissipation. The grid size is estimated based upon

the volume of each cell.

Sample of Field Functions Estimating Grid Size

DES SimulationLength Ratio = Turbulent Length Scale

Grid Size

Effect Of Wake Refinement

DES Simulation

Overall drag value not real sensitive to the

number of inner iteration. It has been seen

that grid density, and refinement locations are

more critical in getting accurate drag values.

13 million Cells

16 million Cells

Commercial Vehicle Example

• Steady, URANS provided

similar results.

• All results with in tolerance of

expectation to wind tunnel

measurements.

Accuracy within 1%!

DES Simulation

*Not actual truck used in study

Sample Result: DES Trend Studies

Results for multiple modifications to underbody.

DES Simulation of an SUV

Number of Cells 160 million

Number of CPUs 512

Turn-around Time 36 hours

2-Layer Modeling

Near Wall

Rigid Body Motion

DES Simulation

Rotating Reference Frame vs Rigid Body Motion

Fan Test Rig Performance

Key factors in fan performance

– MRF can provide good results for

fan performance but does

depend on reference frame

location.

– Low flow rates, MRF has

problems correctly getting

pressure rise.

– RBM does correlate better to in

the transition zone, as well as the

low flow rates.

– RBM also improve thermal

distribution at the outlet of the

fan.

MRF Results

Influence of Inner Sweeps and Relaxation

Factors: 4.5X Speed Up

DES SimulationOptimum settings were seen using 5 inner sweeps, and

Velocity/Pressure URF=0.8. With these settings, run time

was roughly twice as long as the steady state results.

Rigid Body Motion Setup

• Startup

– Initialize solution using steady,

MRF solution

– Enable unsteady solver

– Switch from reference frame to a

defined motion.

DES Simulation

Rigid Body Motion Setup

• Motion/Reference frames are

defined under “Tools” section.

DES Simulation

Vehicle Passing

• Setup for vehicle passing

is the same for rotating

components.

– Motion is just defined as

translation instead of rotation.

– Studies are being done that

include wheels rotating as

vehicle passes.

– Wake interaction important,

hence DES simulation is

recommended.

DES Simulation

Vehicle Passing

• More complex passing

requires more complex

grid motion.

– Studies have used rotating

regions to aid in simulation of

overtaking.

– Mesh morphing has been

used to change ride height.

– Overlapping grids can

simplify grid motion in the

future.

DES Simulation

Sample Mesh Morphing Application

Roof Deflector

• This is an example where it is important

to run the mesh morpher as part of the

unsteady analysis.

• Displacement exaggerated for

animation

• Displacement can be calculated

coupled to stress code, or using

internal stress solver of STAR-CCM+

DES Simulation

• Unsteady simulation is being used today on production work.

– Results from unsteady simulations have shown good correlation with

experimental data.

– Reasonable turn-around times are being achieved

• It is important to understand the difference in settings between steady and

unsteady solver.

• It is important to properly choose time step appropriate to the desired physics being

solved.

– Rigid body motion can easily be defined and run.

– Fluid solid interaction is being used coupled to mesh morphing to simulate

displacement of parts.

– In the future, overlapping grids will be available in STAR-CCM+ which will

simplify complex motion.

Summary

DES Simulation

Thank You

DES Simulation