By using ModelCenter from Phoenix Integration as an integration platform, Pointwise to generate the meshes, and Altair’s AcuSolve to run the CFD calculations, push-button design optimization was achieved for the front wing of an F-1 car. 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 geometry model that can be used for further analysis.
The front wing of a Formula 1 car looks more like a piece of modern art instead of one of the greatest contributors to the downforce and handling of the vehicle. Current front wing designs are intricate and no two are the same, a testament to the complexity of the flowfield experienced by each vehicle as it makes its 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.
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
The presenters of this webinar are the co-authors of a paper originally planned for presentation at SAE WCX: Ilya Tolchinsky (Phoenix Integration), Travis Carrigan (Pointwise), and Josh Dawson (Pointwise).
Update 26 Apr 2020: We have been notified that the SAE WCX paper on which this webinar is based, CAD-Based Optimization of a Race Car Front Wing, has been identified as one of the best from the event and will be published in a future issue of the SAE International Journal of Advances and Current Practices in Mobility.
This webinar is available on-demand – meaning right now. So why not take some time to watch it?