Written by Megan Jenkins on May 21, 2019
February 4th, 2019
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My name is Christopher Vrettos, and I have just completed my BSc in Mechanical Engineering at the University of Cape Town, South Africa. This year I was required to undertake a “Final Year Project”—a full-year project designed to show one’s competencies as an engineer.
I proposed my own project, which was titled “Investigation of the Drag Reduction System (DRS) on an open-source Formula One car”.
This project was based on the Perrinn Formula One car’s rear wing design and simulation from 2014, which was shared on SimScale. My supervisor was Professor Pieter Rousseau.
The project aimed to determine the relationship between the flap angle and the aerodynamic forces/coefficients (drag and downforce) generated by the rear wing of a Formula 1 car at different (low) speeds, and hence determine whether there was an optimum flap angle for the DRS. I defined the optimum flap angle as the angle at which the drag force on the rear wing was minimum. Then I built a scale model of the rear wing and conducted experiments in a wind tunnel to measure the forces at different flap angles/airspeeds.
I used CFD (computational fluid dynamics) simulation technology available on SimScale which helped me design my experiments in the wind tunnel. In addition, they also allowed me to generate a set of theoretical results which I could then compare with the experimental results.
I conducted preliminary simulations to determine the effect of the geometric simplifications (shown in Figure 2) on the expected results. I also used CFD simulations to choose the scale of the model by determining the maximum forces for each scale and to identify the effect of the wind tunnel’s walls on the expected results (compared to free-stream conditions). The selected airspeeds and flap angles were also investigated to obtain a set of theoretical results. Experimental testing in the wind tunnel began only after all simulations had been completed.
I used hex-dominant parametric meshes because hex cells provide more accurate results in general and a parametric mesh is easily customizable. I based my meshes on the SimScale tutorial. However, I had to change the cross-sectional area by modifying the background mesh box’s cell count and the mesh refinements. The resultant meshes had the same cross-sectional area as the wind tunnel.
I chose incompressible fluid dynamics simulations because of the low airspeeds. Fluid flow may be treated as incompressible at Mach numbers less than 0.3. Since the maximum airspeed would be 38.5 m/s—a Mach number of 0.113—the incompressible assumption could be made. This would simplify the simulations and reduce their runtimes. Different meshes would represent different flap angles. I ran multiple analyses on each mesh to test different airspeeds and selected the aerodynamic forces on the rear wing as one of the outputs.
The main challenge when working on the SimScale platform was to shrink the cross-sectional area of the mesh while preserving the quality of the mesh and stability of the simulations from the SimScale tutorial. I was able to overcome this challenge by using a “trial and error” approach—I tested different cell counts and mesh refinements until I found a configuration which generated a high-quality mesh.
The CFD simulations performed well and succeeded in generating the required results. In total, I conducted around 70 simulation runs. For the final analyses, I generated a theoretical set of results, with an average runtime of around 15 minutes on 32 cores.
The results were realistic. As expected, the aerodynamic forces decrease as the flap angle decrease. I then compared these results with the real-world results from the wind tunnel.
My overall impression of the SimScale platform was positive. Using SimScale “on the run” was incredibly convenient—I could arrive at the university campus early in the morning, start simulation or meshing jobs and then attend lectures while the jobs were running on the SimScale servers. If I hadn’t used SimScale, I would have had to find a more powerful computer, as I currently own a seven-year-old laptop. Additionally, I would have had to find a method of storing, analyzing, and backing up the simulation results. With SimScale, this was not a problem because all the CAD models, meshes and simulation results were stored in the cloud and accessible anywhere at any time.
Using SimScale in my project allowed me to design my experiments in the wind tunnel and compare my experimental results with another similar set of results. I could analyze the DRS in greater detail and determine the effects of external factors on the experimental results.
In the industry, to improve the correlation between the theoretical and experimental results—especially with regards to the drag forces—more simulations would be conducted at different friction factors to obtain more accurate drag force results. However, I did not run these analyses for this project due to time limitations.
SimScale helped me design my experiments in the wind tunnel and gave me a set of theoretical results which I could compare with my experimental results. This allowed me to analyze and discuss my experimental results in detail, which helped create a well-rounded and thorough final-year project. If you’re a student, I encourage you to join the Academic Program and experience all the benefits by yourself. You’ll love the benefits.
If you’d like to learn how to set up your own simulations with SimScale, you can watch the free webinar recording about the Formula One season 2017.
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Written by Megan Jenkins on May 21, 2019
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