DrivAerStar: An Industrial-Grade CFD Dataset for Vehicle Aerodynamic Optimization

Jiyan Qiu, Lyulin Kuang, Guan Wang, Yichen Xu, Leiyao Cui, Shaotong Fu, Yixin Zhu, Ruihua Zhang

Keywords: Vehicle aerodynamics optimization, Computational Fluid Dynamics (CFD), Machine learning, DrivAerStar dataset, Industrial-grade simulations, Data-driven design, Automotive engineering, High-fidelity simulations, Wind tunnel validation, Aerodynamic coefficients

TL;DR: DrivAerStar introduces a validated dataset of 12,000 industrial-grade CFD simulations for automotive aerodynamics, enabling machine learning models to achieve engineering accuracy while drastically reducing computational costs.

Abstract: Vehicle aerodynamics optimization has become critical for automotive electrification, where drag reduction directly determines electric vehicle range and energy efficiency. Traditional approaches face an intractable trade-off: computationally expensive Computational Fluid Dynamics (CFD) simulations requiring weeks per design iteration, or simplified models that sacrifice production-grade accuracy. While machine learning offers transformative potential, existing datasets exhibit fundamental limitations -- inadequate mesh resolution, missing vehicle components, and validation errors exceeding 5% -- preventing deployment in industrial workflows. We present DrivAerStar, comprising 12,000 industrial-grade automotive CFD simulations generated using STAR-CCM+${}^{\textregistered}$ software. The dataset systematically explores three vehicle configurations through 20 Computer Aided Design (CAD) parameters via Free Form Deformation (FFD) algorithms, including complete engine compartments and cooling systems with realistic internal airflow. DrivAerStar achieves wind tunnel validation accuracy below 1.04% -- a five-fold improvement over existing datasets -- through refined mesh strategies with strict wall $y^+$ control. Benchmarks demonstrate that models trained on this data achieve production-ready accuracy while reducing computational costs from weeks to minutes. This represents the first dataset bridging academic machine learning research and industrial CFD practice, establishing a new standard for data-driven aerodynamic optimization in automotive development. Beyond automotive applications, DrivAerStar demonstrates a paradigm for integrating high-fidelity physics simulations with Artificial Intelligence (AI) across engineering disciplines where computational constraints currently limit innovation.

Dataset URL: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/UXVXQV

Code URL: https://github.com/qiujiyan/DrivAerStar

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

Submission Number: 186