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Speed up Aerodynamic Optimization Thanks to Deep Learning Physics: Stellantis RAM Truck Example

Optimizing the shape of an object in a given fluid requires the analysis of the physical flow fields around it. To that aim engineers use Computational Fluid Dynamics (CFD), a method that has been proven to work but is highly time consuming. The Deep Learning Physics technology developed by Extrality enables to leverage on some past data to deliver very fast high-fidelity 3D field predictions. This paper presents an example of its application to a RAM truck (Stellantis) whose training is based on a dataset of 20 CFD simulations, showing positive results. The AI-model achieved less than 5% mean relative error in comparison to the CFD on the drag coefficient, while delivering the full surface and volume fields, and keeping the genericity of the predictions. The major benefits lie in the easy-to-use Extrality platform, accessible from anywhere, allowing any engineer to create a Deep Learning Physics model and perform high fidelity simulations without solver knowledge. Hence, it offers a drastic reduction of turn-around time. Less than 5 minutes are needed from the upload of a geometry to the post-processed results. Besides, it is possible to massively reduce the pre-processing time linked to CFD mesh requirements as the model can ingest the native CAD mesh as input while maintaining the aerodynamic coefficient trends. Since the simulation physical knowledge is already embedded in the AI-model, high-fidelity simulations can be done by any stakeholder involved in the project, simplifying the end-to-end optimization workflow. Moreover, the Extrality technology predicts the physical volume and surface flow fields in a format readable by mainstream visualization softwares. Experimentation conducted on the RAM truck example shows that the Extrality technology has its place in today's industry workflow. The results of the AI-model have been compared to the internal DoE tool used by Stellantis and showed that the AI-model achieves, overall, better predictions and is able to correctly identify the car giving the optimum drag coefficient. In another use case, a tonneau bed cover length optimization, the Extrality technology gave the optimal length within 1 day end-to-end, instead of 3 weeks needed to obtain the same information using a classical CFD method. This opens the door to extensive usage of high-fidelity prediction in early design stages and eventually, to shorter design cycles in the industries.

Document Details

AuthorsVerriere. J Bourgeois. M Yser. P Jeyasingham. S Doroudian. M Guzman-Magana. A
TypeExtended Abstract
Date 18th May 2023
OrganisationsExtrality Stellantis


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