AutoHood3D: A Multi‑Modal Benchmark for Automotive Hood Design and Fluid–Structure Interaction

Vansh Sharma, Harish Jai Ganesh, Maryam Akram, Wanjiao Liu, Venkat Raman

Keywords: Fluid-Structure Interaction (FSI), High-fidelity CFD, Multiphysics Numerical Simulations, Parametric Design, Automotive Design, Generative AI, Text‐to‐geometry Synthesis, Geometric Deep Learning, Graph Neural Networks, Point Clouds, 3D Geometry

TL;DR: AutoHood3D is a multi‑modal dataset of automotive hood variants—combining FSI simulations, STL meshes, natural‑language prompts, and standardized ML benchmarks—for generative engineering design and surrogate modeling.

Abstract: This study presents a new high-fidelity multi-modal dataset containing 16000+ geometric variants of automotive hoods useful for machine learning (ML) applications such as engineering component design and process optimization, and multiphysics system surrogates. The dataset is centered on a practical multiphysics problem—hood deformation from fluid entrapment and inertial loading during rotary‑dip painting. Each hood is numerically modeled with a coupled Large-Eddy Simulation (LES)-Finite Element Analysis (FEA), using 1.2M cells in total to ensure spatial and temporal accuracy. The dataset provides time-resolved physical fields, along with STL meshes and structured natural language prompts for text-to-geometry synthesis. Existing datasets are either confined to 2D cases, exhibit limited geometric variations, or lack the multi‑modal annotations and data structures—shortcomings we address with AutoHood3D. We validate our numerical methodology, establish quantitative baselines across five neural architectures, and demonstrate systematic surrogate errors in displacement and force predictions. These findings motivate the design of novel approaches and multiphysics loss functions that enforce fluid–solid coupling during model training. By providing fully reproducible workflows, AutoHood3D enables physics‑aware ML development, accelerates generative‑design iteration, and facilitates the creation of new FSI benchmarks.

Dataset URL: https://doi.org/10.7910/DVN/6OAFF8

Code URL: https://github.com/vanshs1/AutoHood3D/

Primary Area: AL/ML Datasets & Benchmarks for physics (e.g. climate, health, life sciences, physics, social sciences)

Submission Number: 1484