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 ation
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 ation
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 ation
Initializing The Solution • 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. Size 60 million cells *The Segregated solver is recommended for Steady Solution, 80 20 hours transient simulation processors – Faster per iteration DES Simulation, 128 3 Days – Converges the time step in fewer inner iterations Processors DES Sim imula lation ion
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) High Speed Aero Study 11 Vehicle Speed 140 kph 12 Length Scale 40 mm 13 Time Step 0.001 s =(C12/1000)/(C11/3.6) Formula: 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 ation
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. Example shows excessively high current numbers around the DES Simulation ation passenger and front of the vehicle.
Time Step Influence URANS t=0.001s URANS t=0.01s URANS t=0.0002s Large time step show large wake fluctuation, and increase drag 8
Inner Sweeps/Relaxation Factors Parameter Default Optimum Practical Performance Inner Sweeps 20 3 5-8 Velocity Relaxation Factor 0.7 0.9 0.8 Pressure Relaxation Factor 0.3 0.9 0.4 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. DES Simulation ation
Add Pressure Monitors For Inner Sweep Convergence • Monitors For Solution: – Force on Vehicle – Pressure probes in tire/vehicle wake Look 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 Magnitude Peak velocity should be below 150 m/s. If Higher, raising CdesTimeLim can improve stability DES Simulation ation
Monitoring The Solution DES Simulation ation
Relaxation/Inner Sweep Optimization 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%. DES Simulation ation
Inner Sweeps vs. Change in Time 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 DES Simulation ation 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 Turbulent Length TurbulentLengthScale pow(.09,- Determines Turbulence 1 .25)*sqrt($TurbulentKineticEnergy)/$Speci Scale Length scale for K-Omega ficDissipationRate SST model Grid Size GridSize pow($Volume,1.0/3.0) Estimate Edge Length of 2 cell (based upon cubes) Length Ratio LengthRatio $TurbulentLengthScale/(2*$GridSize) To properly resolve 3 turbulence, length ratio > 1 would be needed 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. DES Simulation ation
Sample of Field Functions Estimating Grid Size Length Ratio = Turbulent Length Scale DES Simulation ation Grid Size
Effect Of Wake Refinement 13 million Cells 16 million Cells 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. DES Simulation ation
Commercial Vehicle Example • Steady, URANS provided similar results. • All results with in tolerance of expectation to wind tunnel measurements. Accuracy within 1%! * Not actual truck used in study DES Simulation ation
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-Laye yer r Modeling Near Wall
Rigid Body Motion DES Simulation ation
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 MRF Results 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.
Influence of Inner Sweeps and Relaxation Factors: 4.5X Speed Up Optimum settings were seen using 5 inner sweeps, and DES Simulation ation 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 ation
Rigid Body Motion Setup • Motion/Reference frames are defined under “Tools” section. DES Simulation ation
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 ation
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 ation
Recommend
More recommend
