1. Interactive design of 2D car profiles with aerodynamic feedback
- Author
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Nicolas Rosset, Guillaume Cordonnier, Regis Duvigneau, Adrien Bousseau, Université Côte d'Azur (UCA), GRAPHics and DEsign with hEterogeneous COntent (GRAPHDECO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Analysis and Control of Unsteady Models for Engineering Sciences (ACUMES), and This work was supported by the European Research Council (ERC) starting grant D3 (ERC-2016-STG 714221) and research and software donations from Adobe Inc.
- Subjects
implicit representation ,Aerodynamics ,Interactive design fluid simulation surrogate model shape optimization neural network implicit representation ,neural network ,shape optimization ,fluid simulation ,Car design ,surrogate model ,Computer Graphics and Computer-Aided Design ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR] ,Interactive design ,OPAL-Meso - Abstract
International audience; The design of car shapes requires a delicate balance between aesthetic and performance. While fluid simulation provides themeans to evaluate the aerodynamic performance of a given shape, its computational cost hinders its usage during the early explorative phases of design, when aesthetic is decided upon. We present an interactive system to assist designers in creating aerodynamic car profiles. Our system relies on a neural surrogate model to predict fluid flow around car shapes, providing fluid visualization and shape optimization feedback to designers as soon as they sketch a car profile. Compared to prior work that focused on time-averaged fluid flows, we describe how to train our model on instantaneous, synchronized observations extracted from multiple pre-computed simulations, such that we can visualize and optimize for dynamic flow features, such as vortices. Furthermore, we architectured our model to support gradient-based shape optimization within a learned latent space of car profiles. In addition to regularizing the optimization process, this latent space and an associated encoder-decoder allows us to input and output car profiles in a bitmap form, without any explicit parameterization of the car boundary. Finally, we designed our model to support pointwise queries of fluid properties around car shapes, allowing us to adapt computational cost to application needs. As an illustration, we only query our model along streamlines for flow visualization, we query it in the vicinity of the car for drag optimization, and we query it behind the car for vortex attenuation.
- Published
- 2023