DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction
Massachusetts Institute of Technology（麻省理工学院）；Technical University of Munich（慕尼黑技术大学）；Department of Mechanical Engineering（机械工程系）；Department of Computer Science（计算机科学系）
This study introduces DrivAerNet, a large-scale high-fidelity CFD dataset of 3D industry-standard car shapes, and RegDGCNN, a dynamic graph convolutional neural network model, both aimed at aerodynamic car design through machine learning. DrivAerNet, with its 4000 detailed 3D car meshes using 0.5 million surface mesh faces and comprehensive aerodynamic performance data comprising of full 3D pressure, velocity fields, and wall-shear stresses, addresses the critical need for extensive datasets to train deep learning models in engineering applications. It is 60\% larger than the previously available largest public dataset of cars, and is the only open-source dataset that also models wheels and underbody. RegDGCNN leverages this large-scale dataset to provide high-precision drag estimates directly from 3D meshes, bypassing traditional limitations such as the need for 2D image rendering or Signed Distance Fields (SDF). By enabling fast drag estimation in seconds, RegDGCNN facilitates rapid aerodynamic assessments, offering a substantial leap towards integrating data-driven methods in automotive design. Together, DrivAerNet and RegDGCNN promise to accelerate the car design process and contribute to the development of more efficient cars. To lay the groundwork for future innovations in the field, the dataset and code used in our study are publicly accessible at https://github.com/Mohamedelrefaie/DrivAerNet.
DrivAer Transformer: A high-precision and fast prediction method for vehicle aerodynamic drag coefficient based on the DrivAerNet++ dataset
Hubei Key Laboratory of Advanced Technology of Automotive Parts, Wuhan University of Technology, Wuhan, China（湖北汽车零部件先进技术重点实验室，武汉理工大学，中国武汉）；Wuhan National Laboratory for Optoelectronic, Huazhong University of Science（武汉光电国家实验室，华中科技大学）
CarBench: A Comprehensive Benchmark for Neural Surrogates on High-Fidelity 3D Car Aerodynamics
Massachusetts Institute of Technology（麻省理工学院）；Schwarzman College of Computing, Massachusetts Institute of Technology（斯克里文计算机学院，麻省理工学院）；Future Product Innovation, Toyota Research Institute（丰田研究机构未来产品创新部）
TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks
Department of Mechanical Engineering（机械工程系）；Massachusetts Institute of Technology（麻省理工学院）；Department of Computer Science（计算机科学系）；Technical University of Munich（慕尼黑技术大学）
TripOptimizer: Generative 3D Shape Optimization and Drag Prediction using Triplane VAE Networks
Department of concepts and methods development in virtual fields, AUDI AG（虚拟领域概念与方法发展系，奥迪公司）；Technology and Bionics Faculty, Rhine-Waal University of Applied Sciences（技术与生物科学系，莱茵-瓦尔大学）；Department of Mechanical Engineering, Massachusetts Institute of Technology（机械工程系，麻省理工学院）；Schwarzman College of Computing, Massachusetts Institute of Technology（施瓦茨曼计算学院，麻省理工学院）
AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院机械工程系）；Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院电气工程与计算机科学系）；Department of Computer Science, Technical University of Munich, Munich, Germany（慕尼黑技术大学计算机科学系）
Towards aerodynamic surrogate modeling based on $β$-variational autoencoders
Theoretical and Computational Aerodynamics Branch, Spanish National Institute for Aerospace Technology (INTA)（西班牙航空航天技术国家研究所理论与计算空气动力学分支）；Subdirectorate General of Terrestrial Systems, Spanish National Institute for Aerospace Technology (INTA)（西班牙航空航天技术国家研究所陆地系统总处）；Department of Aerospace Engineering, Universidad Carlos III de Madrid（马德里卡洛斯三世大学航空航天工程系）
General Electric Research（通用电气研究院）；University of Notre Dame（圣母大学）
One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durability, safety and manufacturing, which make the design process sequential and iterative.The sequential nature of these iterations forces a long design cycle time, ranging from several months to years. Due to the reactionary nature of these iterations, little effort has been made to accumulate data in a manner that allows for deep exploration and understanding of the total design space. This is exemplified in the proc…
