FSAE Vehicle Aerodynamics John Strudel Dr. Mesbah Uddin ...
Transcript of FSAE Vehicle Aerodynamics John Strudel Dr. Mesbah Uddin ...
FSAE Vehicle Aerodynamics
Setup, Simulation, and Analysis using Star CCM+
John Strudel
Dr. Mesbah Uddin
Chunhui Zhang
University of North Carolina at Charlotte
Department of Mechanical Engineering
Motorsports Engineering
MEGR 3242-Applied Vehicle Aerodynamics
3/25/2018
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Table of Contents
Abstract 2
Objective 2
Introduction 2
Results and Discussion 4
Conclusion 18
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Abstract
The Formula SAE (FSAE) Project demonstrates the importance of simulation setup, mesh
refinement, and parameter monitoring. Using Star CCM+ and advanced setup, monitoring, and
visualization techniques learnt in class, the FSAE car was meshed and simulated to resolve the
forces of lift and drag induced by the vehicle’s aerodynamic devices. The results of the
simulation were analyzed and parsed for veracity. The main take away for the engineer is the
minimal drag penalty for the downforce addition of the front wing assembly.
Objective
The objective of this project was to demonstrate the setup, meshing, and simulation of an
external vehicle aerodynamics simulation using Star CCM+ version 12.06.011. Using the
techniques learnt in the classroom, the generated meshes were examined and used to improve
simulation results.
Introduction
To setup an external vehicle aerodynamics simulation the engineer is required to develop and
refine a precision CAD model for use in simulation. Complex vehicle geometry needs to be
simplified into important regions of interest. For this project, the CAD geometry was provided
and the meshing and physics setup procedure were required.
To develop a representation of the vehicle being examined a surface wrapper operation was done
to generate a detailed representation of the external surface. The base size was set to 8 mm and
the minimum surface size was adjusted to 5% of base size. The volume of interest was set to
External. A curvature control was added to the surface wrapper operation and all of the curves of
interest on the vehicle were selected. The curve control was used to set the target and minimum
surface size of the areas of interest. Contact preventions were created to prevent the front wing
from being improperly meshed. The simulation was saved and the operation was executed.
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Figure 1: Surface Wrapper operation close up of side pod inlet area.
Figure 2: Surface wrapper over vehicle.
To examine the external aerodynamic forces present on the vehicle of interest a wind tunnel
needs to be developed to capture all of the flow characteristics presented by the vehicle. To
develop the tunnel a new part was created in the shape of a block. The block was sized such that
the velocity inlet of the tunnel was 5 time the characteristic length of the vehicle forward and the
outlet was 15 times the length rearward. To reduce the computing requirements a half car model
was examined. The tunnel was set to fall along the longitudinal center plane of the vehicle and
extend 5 times the characteristic width of the vehicle out laterally. The final parameter of the
tunnel requires an offset to compensate for the tire contact with the ground. The offset is set to 4
mm and ride height was recorded. The height of the tunnel was set to 7 times the characteristic
height of the vehicle. The tunnel was created, the simulation was saved.
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A Boolean operation was executed to create a fluid volume for the simulation. The input parts of
the operation were the wind tunnel and surface wrapper. The output part of the operation was the
fluid volume to be analyzed. This operation ignores the geometry which is not required and
develops a fluid volume for meshing.
The fluid volume was meshed utilizing an automated mesh procedure. The meshers selected for
this operation were; Surface Remesher, Automatic Surface Repair, Polyhedral Mesher, and
Prism Layer Mesher. The base size was set to 24 mm with 6 prism layers. Using Custom
controls, surfaces and curvatures as well as near and far volume extents were controlled to
promote mesh generation and improve simulation results. Figure 3 shows the near car volume
control and the resultant Automated Mesh.
Figure 3: Polyhedral volume mesh visualization including a near vehicle volume control.
Results and Discussion
Mesh Optimization
The resulting mesh had surface defects present in the form of insufficient curvature control and
the target surface size was too large to accurately represent the wing profile. After refining mesh
parameters, the surface of the vehicle being examined was visually similar to the intended
surfaces of the CAD geometry. The initial volume mesh was 4.5 million cells and took around 2
hours for the mesh operation to complete. The final mesh has 6.3 million cells and took more
than 4 hours to execute the operation. The simulation was iterated for ~500 iterations when target
parameters of interest were converging around a solution and some flow could be visualized.
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Figure 4: Mesh defects from initial solution.
Figure 4 shows some of the surface defects presents from the initial mesh settings. The optimized
surface and curvature controls were used to improve the overall mesh. Figure 5 shows the same
area where defects were present after remeshing. The additional time required to mesh was worth
the improvement in surface quality. Additionally the residuals converged faster after refinement.
Figure 5: Repaired mesh after improving surface and curvature controls.
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Figure 6: Iterations run for FSAE Project simulation.
After initial iterations the results and flow visualization was used to analyze the solution. An
anomaly was noticed in the drag and downforce plots which required a physics analysis. It was
determined that wheel vectors were incorrectly assigned and the wheels were rotating the wrong
direction. The physics values were corrected using local coordinate systems on the individual
wheels. An additional ~1000 iterations were run until parameters of interest were converged on a
solution. Figure 7 shows the results of the incorrect wheel rotation causing a higher body
downforce value than expected. Once the correct wheel rotation was configured the results were
closer to anticipated values.
Figure 7: Downforce monitor plot showing the incorrect wheel rotation results at less than 500 iterations.
Once the monitored parameters were determined to be converged, flow visualization and report
monitors were used to analyze the results of the simulation. Figure 8 is the coefficient of pressure
plot projected onto the symmetry plane and surfaces of the vehicle. The low and high pressure
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areas of interest are visible. These scenes provide the engineer with a tool for developing flow
manipulation strategies. The low pressure regions of the pressure coefficient plot are the areas of
interest. Downforce and drag are developed by pressure differentials and knowing the position of
low pressure areas allows improved control over system balance.
Figure 8: Coefficient of pressure plotted on the symmetry plane and surface profile of the vehicle.
Figure 9 plots the pressure coefficient on the surface of the vehicle for pressure locations. The
influence of the tires on the overall downforce of the vehicle is seen as deep blue color on the
upper portion of the tires.
Figure 9: The coefficient of pressure plotted on the surface of the vehicle.
The pressure plot gives the engineers a method of determining where to focus the analysis and
optimization efforts. The front wing generates a significant portion of the downforce. The
velocity profile is examined in Figure 10. The ground effect of the front wing is seen to benefit
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the wing in downforce generation. Additional testing of vehicle ride height would be beneficial
to the development of the aerodynamic subsystem.
Figure 10: Velocity field near the front wing profile.
Additionally, Figure 10 shows the location of flow separation along the trailing edge of the
airfoil. The upper wing also displays a small area of flow separation which indicates that attack
angle analysis would be beneficial to the development of the subsystem. Figure 11 shows the
velocity field around the front tire. It is visible that the rotation of the tire is contributing to the
lift and drag forces of the vehicle. It appears that the inverse pressure gradient is contributing to
the flow separation seen in figure 10. If only race cars did not need wheels.
Figure 11: Velocity vector field near the front tire.
Analyzing the plane of symmetry many of the dominate flow characteristics were assessed.
Figures 12 through 16 show the velocity vector fields around the entire vehicle. The high
velocity visualized under the vehicle shows the areas where the lift forces will be the greatest.
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Additionally, the stagnation point at the tip of the nose and the base pressure area at the rear of
the vehicle can be visualized indicating the presence of drag forces. The influence that the single
one inch steel bar has on the flow is interesting. The bar seems to have an influence on all except
the lateral velocity field.
Figure 12: Streamlines projected on the symmetry plane with the velocity magnitude scale.
Figure 13: Velocity magnitude on the symmetry plane.
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Figure 14: Velocity vector field in the i-direction.
Figure 15: Velocity in the j-direction.
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Figure 16: Velocity plotted n the k-direction on the center plane.
The isosurface visualization in Figure 17 represents the velocity vector in the longitudinal
direction. The areas which appear to trail the vehicle are areas where the velocity is less than 3
m/s. The image is a good representation of the drag forces acting on the vehicle. The isosurface
visually looks like a blanket of air being dragged along behind the car.
Figure 17: Isosurface of the velocity vector in the i-direction.
The isosurface visualization is best accompanying the drag report plot in Figure 18. The
influence of the body on the total drag of the vehicle is clearly visible. For the 15 pounds of
downforce which the front wing developed, seen in Figure 7, there was only a 2 pound drag
force penalty. This airfoil profile was optimal for the application.
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Figure 18: Drag force plot of body and wing drag forces.
A better representation of the lift and drag profiles of the entire vehicle, assuming symmetry, a
lift and drag coefficient was calculated in the program. The calculation of coefficients required
the frontal area input of 0.370 𝑚2 and the target air density for a FSAE competition of
1.162𝑘𝑔/𝑚3. Figure 19 shows the coefficient of drag calculated to be 0.55 and Figure 20 shows
the coefficient of lift was calculated to be 0.44.
Figure 19: Coefficient of Drag plot.
These values calculated seem to be reasonable for the system. Full car simulations would verify
these results. Force coefficients assist the design engineer with simulation of aerodynamic forces
during kinematic analysis. Additionally, utilization of a rear wing and some method of
preventing the tires from diluting the diffuser airflow could improve the lift profile.
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Figure 20: Coefficient of Lift plot.
Cooling air flow was monitored at the inlet to the side pod. Figure 21 plots the mass air flow into
the side pod. An average value of 600 grams per second was calculated. This is sufficient airflow
for the FSAE engine cooling requirements.
Figure 21: Side pod mass air flow plot.
While the side pod inlet looks really nice, Figure 22 shows the velocity field vectors near the
inlet. The side pod actually develops positive lift. A new profile for the upper inlet is
recommended.
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Figure 22: Velocity field near the side pod inlet.
Three dimensional streamlines are often a good way to get a high quality visualization of the
characteristic flow over and around the vehicle. Figure 23 shows the velocity vector plotted as
3D streamlines with seeds at the center of the car along the symmetry plane.
Figure 23: 3D Streamlines presented as tubes.
Figure 24 plots the same 3D streamlines with the seed from the diffuser outlet. The dilution of
the diffuser flow with air from the rear tire is visible. This is the first visualization which also
shows the low velocity of the flow under the side pod inlet.
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Figure 24: Underbody streamline visualization from diffuser outlet seed.
Figure 25: First try of FSAE Project 1.
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Figure 26: Second Try FSAE Project 1.
Figure 27: Third try FSAE Project 1.
Learning to correctly setup the mesh and physics for an accurate CFD simulation is a time
consuming and difficult process. Each simulation requires detailed monitoring and configuration.
After running multiple iteration in multiple configurations the final setup was run with a clear
solution to develop a singular result. Figure 6 is the final configuration iterations and I still made
the mistake of not checking the wheel vectors prior to allowing the simulation to run. Figure 28
shows the results of the simulation in Figure 27. The influence of the tires rotating in the
incorrect direction is visible.
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Figure 27: Simulation results from incorrect wheel rotation.
Additional practice and experience is required to improve the engineers’ abilities with the
simulation software. Figure 28 was an attempt at an “oil flow” plot on the surface of the front
wing. The use of visualizations which are well designed and accurately display the flow and
forces acting on the vehicle are important to the quality of an aerodynamic simulations results.
Figure 28: Oil Flow on the surface of the front wing.
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Conclusion
Utilizing the appropriate setup, refinement, and monitoring of external vehicle aerodynamics is
critical to the quality of the results. In a real world experiment the engineer would expect
different results because of the complexities of the small scale turbulence and tunnel design.
Monitoring surface pressures in area of interest could be used to validate and improve simulation
results. Real world experiments are difficult to obtain the detail of the results which can be
visualized using simulation software such as Star CCM+