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Simulation of A-Pillar/Side Glass Flows For Bluff SUV Geometries
- 1. 5 th MIRA International Vehicle Aerodynamics Conference 13-14 October 2004 Heritage Motor Centre Warwick UK Simulation of A-Pillar/Side Glass Flows For Bluff SUV Geometries Adrian Gaylard Aerodynamics Jaguar and Land Rover
- 2. Introduction Background Simulations Results Conclusions Contents
- 3. Introduction Potential applications of CFD Forces and Moments
- 4. Introduction Potential applications of CFD Thermal Management
- 5. Introduction Potential applications of CFD Brake Cooling
- 6. Introduction Potential applications of CFD Soiling
- 7. Introduction Potential applications of CFD Water Management
- 8. Introduction Potential applications of CFD Why focus on A-Pillar /Side Glass?
- 9. A-Pillar Flow Structure (after Haruna et al ) Background
- 11. CFD Technique Exa PowerFLOW ™ Tends to over-predict A-pillar vortex strength and size RANS methods tend to under-predict A-pillar vortex strength and size (Murad et al ) Background
- 12. Simulation Parameters Simulations Mesh Structure
- 13. Simulation Parameters Simulations Turbulence Model
- 14. Simulation Parameters Simulations Boundary Layer
- 15. Mesh Structure and Resolution Simulations Low Resolution 10-12 mm 5-6 mm
- 16. Mesh Structure and Resolution Simulations Aerodynamics 10-12 mm 5-6 mm 2.5-3 mm
- 17. Mesh Structure and Resolution Simulations Aeroacoustics 10-12 mm 5-6 mm 2.5-3 mm 1.5 mm
- 18. Surface Flow Visualisation Results Low Res. Aeroacoustic Aerodynamic 5 mm 2.75 mm 1.5 mm
- 19. Surface Static Pressure Results Low Res. Aeroacoustic Aerodynamic
- 20. Surface Static Pressures Results
- 21. Surface Static Pressures Results
- 22. Surface Static Pressures Results
- 23. Surface Static Pressures Results
- 24. Surface Static Pressures Results
- 25. Surface Static Pressures Results
- 26. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 27. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 28. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 29. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 30. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 31. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 32. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 33. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 34. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 35. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 36. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 37. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 38. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 39. Near-Surface Velocity Profiles Results Low Resolution simulation (A) Aeroacoustic simulation (D) Experiment
- 40. Surface Noise Spectra Results
- 41. Surface Noise Spectra Results
- 42. Surface Noise Spectra Results
- 43. Surface Noise Spectra Results
- 44. Surface Noise Spectra Results
- 45. Surface Noise Spectra Results
- 46. Surface Noise Spectra Results
- 47. Surface Noise Spectra Results
- 49. Sensitivities spatial resolution characteristics of the turbulence model boundary layer length scale Conclusions II
Editor's Notes
- 1 Thank: Jaguar Land Rover for permission to publish this work Graham Bambrook (boundary layer rake construction and experimental support) Chao Peng (surface noise spectra measurement and data processing)
- 1
- 1 Improving prediction of forces and moments Bluff bodies still present significant challenges Engineering Design processes increasingly occur in parallel shared models, why not shared simulations? Need for simulations that go beyond traditional aerodynamics Acoustics Thermal management Brake Cooling Water management Washer/Wiper systems Requirement for local accuracy First step – A-Pillar/Side Glass Can we currently obtain flow structure/pressure simulations of sufficient accuracy to support (principally) wind noise reduction activities?
- 1 Improving prediction of forces and moments Bluff bodies still present significant challenges Engineering Design processes increasingly occur in parallel shared models, why not shared simulations? Need for simulations that go beyond traditional aerodynamics Acoustics Thermal management Brake Cooling Water management Washer/Wiper systems Requirement for local accuracy First step – A-Pillar/Side Glass Can we currently obtain flow structure/pressure simulations of sufficient accuracy to support (principally) wind noise reduction activities?
- 1 Improving prediction of forces and moments Bluff bodies still present significant challenges Engineering Design processes increasingly occur in parallel shared models, why not shared simulations? Need for simulations that go beyond traditional aerodynamics Acoustics Thermal management Brake Cooling Water management Washer/Wiper systems Requirement for local accuracy First step – A-Pillar/Side Glass Can we currently obtain flow structure/pressure simulations of sufficient accuracy to support (principally) wind noise reduction activities?
- 1 Improving prediction of forces and moments Bluff bodies still present significant challenges Engineering Design processes increasingly occur in parallel shared models, why not shared simulations? Need for simulations that go beyond traditional aerodynamics Acoustics Thermal management Brake Cooling Water management Washer/Wiper systems Requirement for local accuracy First step – A-Pillar/Side Glass Can we currently obtain flow structure/pressure simulations of sufficient accuracy to support (principally) wind noise reduction activities?
- 1 Improving prediction of forces and moments Bluff bodies still present significant challenges Engineering Design processes increasingly occur in parallel shared models, why not shared simulations? Need for simulations that go beyond traditional aerodynamics Acoustics Thermal management Brake Cooling Water management Washer/Wiper systems Requirement for local accuracy First step – A-Pillar/Side Glass Can we currently obtain flow structure/pressure simulations of sufficient accuracy to support (principally) wind noise reduction activities?
- 1 Improving prediction of forces and moments Bluff bodies still present significant challenges Engineering Design processes increasingly occur in parallel shared models, why not shared simulations? Need for simulations that go beyond traditional aerodynamics Acoustics Thermal management Brake Cooling Water management Washer/Wiper systems Requirement for local accuracy First step – A-Pillar/Side Glass Can we currently obtain flow structure/pressure simulations of sufficient accuracy to support (principally) wind noise reduction activities?
- 1 Challenging local area to get right Geometrically complex Fluid dynamically complex A-Pillar vortex Attached flow region Door mirror wake (not shown for clarity)
- 1 Land Rover Discovery 3 (LR3 in US) Detailed “Top Hat” Blanked cooling intakes Blanked wheels Flushed panel gaps Smoothed and sealed underbody
- 1 Why Exa PowerFLOW ™? Standard aerodynamics solver at Jaguar Land Rover. Capable of delivering good aerodynamics simulations (Joe Amodeo paper). Inherently unsteady solver Process advantages – no volume meshing required by user! Has weaknesses over-prediction of vortex strength and size So do RANS codes under-prediction of A-pillar vortex strength/size
- 1 Review of 7 simulations 3 main factors varied Resolution/Mesh structure 18 million – 27 million volume elements 1.4 million to 1.8 million surface elements More detail on next slide Turbulence model Boundary layer model Inlet velocity varied to match AAWT and AWT experiments Critical Re exceeded for all simulations (a-pillar radius) AAWT cases matched for temperature also.
- 1 Review of 7 simulations 3 main factors varied Resolution/Mesh structure 18 million – 27 million volume elements 1.4 million to 1.8 million surface elements More detail on next slide Turbulence model Boundary layer model Inlet velocity varied to match AAWT and AWT experiments Critical Re exceeded for all simulations (a-pillar radius) AAWT cases matched for temperature also.
- 1 Review of 7 simulations 3 main factors varied Resolution/Mesh structure 18 million – 27 million volume elements 1.4 million to 1.8 million surface elements More detail on next slide Turbulence model Boundary layer model Inlet velocity varied to match AAWT and AWT experiments Critical Re exceeded for all simulations (a-pillar radius) AAWT cases matched for temperature also.
- 1 Nested mesh refinement regions, voxel dimension is halved from previous. Region depth: 15-20 voxel lengths Door Mirror resolution zones not shown for clarity Whole car regions based on mesh offsetting Low Resolution 10mm mesh: whole vehicle 5mm mesh: bonnet/plenum/screen/A-pillar/E-pillar Aerodynamics 11mm mesh: whole vehicle plus stagnation zone/wake 5.5mm mesh: whole vehicle including wheels 2.75mm mesh: plenum/screen/A-pillar/Side glass/B-pillar Aeroacoustics 12 mm mesh: whole vehicle plus stagnation zone 6mm/3mm/1.5mm mesh: plenum/screen/A-pillar/Side glass/B-pillar
- 1 Nested mesh refinement regions, voxel dimension is halved from previous. Region depth: 15-20 voxel lengths Door Mirror resolution zones not shown for clarity Whole car regions based on mesh offsetting Low Resolution 10mm mesh: whole vehicle 5mm mesh: bonnet/plenum/screen/A-pillar/E-pillar Aerodynamics 11mm mesh: whole vehicle plus stagnation zone/wake 5.5mm mesh: whole vehicle including wheels 2.75mm mesh: plenum/screen/A-pillar/Side glass/B-pillar Aeroacoustics 12 mm mesh: whole vehicle plus stagnation zone 6mm/3mm/1.5mm mesh: plenum/screen/A-pillar/Side glass/B-pillar
- 1 Nested mesh refinement regions, voxel dimension is halved from previous. Region depth: 15-20 voxel lengths Door Mirror resolution zones not shown for clarity Whole car regions based on mesh offsetting Low Resolution 10mm mesh: whole vehicle 5mm mesh: bonnet/plenum/screen/A-pillar/E-pillar Aerodynamics 11mm mesh: whole vehicle plus stagnation zone/wake 5.5mm mesh: whole vehicle including wheels 2.75mm mesh: plenum/screen/A-pillar/Side glass/B-pillar Aeroacoustics 12 mm mesh: whole vehicle plus stagnation zone 6mm/3mm/1.5mm mesh: plenum/screen/A-pillar/Side glass/B-pillar
- 1 Experiment Fluorescene MIRA FSWT (27 m/s) Simulations A-Pillar Vortex General over-prediction of vortex size Higher resolution associated with increased over-prediction Door Mirror Wake disturbance length Over-predicted by low resolution simulation Improved prediction associated with increased resolution General accuracy Flow structure reasonably captured Different trends in different flow regions Associated with both resolution and turbulence model
- 1 Experiment MIRA FSWT (27 m/s) Pressures on Side Glass only Simulations Low resolution simulation showed reasonable A-pillar vortex structure Negative pressures (red) over-predicted nevertheless
- Trend of experimental data reasonably well captured Low resolution model (A) worst Aeroacoustics model (D) best, = -0.13 (all tappings) Accuracy varies with flow region Door Mirror wake region generally best captured Best simulation varies with flow zone Higher resolution cases typically progressively deviate from experiment in the A-pillar vortex region In attached/Door Mirror wake regions higher resolution methods typically do best. Exception: combination of high res. (1.5mm) with local boundary layer length scale. (F) Improved turbulence model associated with better results at the same resolution level (A vs. G) 1
- Trend of experimental data reasonably well captured Low resolution model (A) worst Aeroacoustics model (D) best, = -0.13 (all tappings) Accuracy varies with flow region Door Mirror wake region generally best captured Best simulation varies with flow zone Higher resolution cases typically progressively deviate from experiment in the A-pillar vortex region In attached/Door Mirror wake regions higher resolution methods typically do best. Exception: combination of high res. (1.5mm) with local boundary layer length scale. (F) Improved turbulence model associated with better results at the same resolution level (A vs. G) 1
- Trend of experimental data reasonably well captured Low resolution model (A) worst Aeroacoustics model (D) best, = -0.13 (all tappings) Accuracy varies with flow region Door Mirror wake region generally best captured Best simulation varies with flow zone Higher resolution cases typically progressively deviate from experiment in the A-pillar vortex region In attached/Door Mirror wake regions higher resolution methods typically do best. Exception: combination of high res. (1.5mm) with local boundary layer length scale. (F) Improved turbulence model associated with better results at the same resolution level (A vs. G) 1
- Trend of experimental data reasonably well captured Low resolution model (A) worst Aeroacoustics model (D) best, = -0.13 (all tappings) Accuracy varies with flow region Door Mirror wake region generally best captured Best simulation varies with flow zone Higher resolution cases typically progressively deviate from experiment in the A-pillar vortex region In attached/Door Mirror wake regions higher resolution methods typically do best. Exception: combination of high res. (1.5mm) with local boundary layer length scale. (F) Improved turbulence model associated with better results at the same resolution level (A vs. G) 1
- Trend of experimental data reasonably well captured Low resolution model (A) worst Aeroacoustics model (D) best, = -0.13 (all tappings) Accuracy varies with flow region Door Mirror wake region generally best captured Best simulation varies with flow zone Higher resolution cases typically progressively deviate from experiment in the A-pillar vortex region In attached/Door Mirror wake regions higher resolution methods typically do best. Exception: combination of high res. (1.5mm) with local boundary layer length scale. (F) Improved turbulence model associated with better results at the same resolution level (A vs. G) 1
- Trend of experimental data reasonably well captured Low resolution model (A) worst Aeroacoustics model (D) best, = -0.13 (all tappings) Accuracy varies with flow region Door Mirror wake region generally best captured Best simulation varies with flow zone Higher resolution cases typically progressively deviate from experiment in the A-pillar vortex region In attached/Door Mirror wake regions higher resolution methods typically do best. Exception: combination of high res. (1.5mm) with local boundary layer length scale. (F) Improved turbulence model associated with better results at the same resolution level (A vs. G) 1
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Experiment MIRA FSWT (27 m/s) “ boundary layer rake” Tubes aligned with flow at surface Cannot cope with reversed flows Simulations Comparison of “best” (D) and “worst” (A) simulations #1 Flow “jets” between door mirror and side glass over-prediction of velocity #2-4 Velocity profile captured reasonably. over-prediction of velocity #5-7 Attached flow region High resolution model pretty good Low res. model poor. #9/#12 A-Pillar vortex. Experiment unreliable.
- 1 Simulation D Over-predicted A-pillar vortex Significant error in (average) static pressures Generally (unexpectedly) good agreement reasonable correlation obtained for locations subject to the over-prediction of the size of the separated zone may be due, in part, to the masking of this effect by the noise content in the shear layer ( Sadakata et al ) Largest error at edge of door mirror wake
- 1 Simulation D Over-predicted A-pillar vortex Significant error in (average) static pressures Generally (unexpectedly) good agreement reasonable correlation obtained for locations subject to the over-prediction of the size of the separated zone may be due, in part, to the masking of this effect by the noise content in the shear layer ( Sadakata et al ) Largest error at edge of door mirror wake
- 1 Simulation D Over-predicted A-pillar vortex Significant error in (average) static pressures Generally (unexpectedly) good agreement reasonable correlation obtained for locations subject to the over-prediction of the size of the separated zone may be due, in part, to the masking of this effect by the noise content in the shear layer ( Sadakata et al ) Largest error at edge of door mirror wake
- 1 Simulation D Over-predicted A-pillar vortex Significant error in (average) static pressures Generally (unexpectedly) good agreement reasonable correlation obtained for locations subject to the over-prediction of the size of the separated zone may be due, in part, to the masking of this effect by the noise content in the shear layer ( Sadakata et al ) Largest error at edge of door mirror wake
- 1 Simulation D Over-predicted A-pillar vortex Significant error in (average) static pressures Generally (unexpectedly) good agreement reasonable correlation obtained for locations subject to the over-prediction of the size of the separated zone may be due, in part, to the masking of this effect by the noise content in the shear layer ( Sadakata et al ) Largest error at edge of door mirror wake
- 1 Simulation D Over-predicted A-pillar vortex Significant error in (average) static pressures Generally (unexpectedly) good agreement reasonable correlation obtained for locations subject to the over-prediction of the size of the separated zone may be due, in part, to the masking of this effect by the noise content in the shear layer ( Sadakata et al ) Largest error at edge of door mirror wake
- 1 Simulation D Over-predicted A-pillar vortex Significant error in (average) static pressures Generally (unexpectedly) good agreement reasonable correlation obtained for locations subject to the over-prediction of the size of the separated zone may be due, in part, to the masking of this effect by the noise content in the shear layer ( Sadakata et al ) Largest error at edge of door mirror wake
- 1 Simulation D Over-predicted A-pillar vortex Significant error in (average) static pressures Generally (unexpectedly) good agreement reasonable correlation obtained for locations subject to the over-prediction of the size of the separated zone may be due, in part, to the masking of this effect by the noise content in the shear layer ( Sadakata et al ) Largest error at edge of door mirror wake
- 1
- 1 Fundamentals of CFD apply. No replacement for resolution Turbulence model highly influential Challenges remain Deficiencies will not be resolved by increasing resolution Potential for addressing aeroacoustic development demonstrated