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+