CAD-Based Design Optimization of a Race Car Front Wing | PHOENIX INTEGRATION and POINTWISE
ATTEND the webinar presented by Mr. Ilya Tolchinsky, Lead Application Engineer, Phoenix Integration, Mr. Travis Carrigan, Manager, Business Development and Mr. Joshua Dawson, Business Development at Pointwise, on August 18, 2020 at 10:00 am ET.
In this webinar, Phoenix Integration and Pointwise demonstrate push-button design optimization for the front wing of a race car geometry. The tools and methodology used are highlighted and a design resulting in maximum downforce is identified.
If you have ever seen the front wing of a Formula 1 vehicle you would think you are staring at a piece of modern art and not one of the greatest contributors to the downforce and handling of the vehicle. Current front wing designs are so complex and no two are the same, a testament to the complexity of the flowfield experienced by each vehicle as they make their way around the track. Simplifying the design and looking at just the twist distribution, there is still little to no agreement found. Hence the purpose of this study. Can we identify the optimal twist distribution for the front wing of a race car that maximizes downforce without increasing drag? It turns out that we can.
By using ModelCenter from Phoenix Integration as an integration platform, Pointwise to generate the meshes, and AcuSolve to run the CFD calculations, push-button design optimization was achieved. In fact, the model-centric process was built on parametric geometry so that a single mesh could be mapped effortlessly from one design iteration to the next and the final output is an actual CAD model that can be used for further analysis.
Topics covered in this webinar:
- Formulating the optimization problem including the model and design variables and setting up the integration platform
- Generating a baseline mesh that isolates the area of interest and automating the parametric mapping for each design iteration
- Locating the optimum twist distribution for maximum downforce using a genetic algorithm on a response surface