AFBench: A Large-scale Benchmark for Airfoil Design

TL;DR

AFBench introduces a large-scale, open-source benchmark dataset with 200,000 airfoils and associated labels, enabling the development and evaluation of data-driven, controllable inverse airfoil design methods using multimodal physical parameters.

Contribution

This paper provides the first large-scale benchmark dataset and tasks for airfoil inverse design, including new evaluation metrics and baseline models, facilitating progress in data-driven and controllable airfoil generation.

Findings

Baseline models trained efficiently on RTX 3090 GPU

New tasks enable multimodal physical parameter control

Benchmark dataset supports comprehensive evaluation of methods

Abstract

Data-driven generative models have emerged as promising approaches towards achieving efficient mechanical inverse design. However, due to prohibitively high cost in time and money, there is still lack of open-source and large-scale benchmarks in this field. It is mainly the case for airfoil inverse design, which requires to generate and edit diverse geometric-qualified and aerodynamic-qualified airfoils following the multimodal instructions, \emph{i.e.,} dragging points and physical parameters. This paper presents the open-source endeavors in airfoil inverse design, \emph{AFBench}, including a large-scale dataset with 200 thousand airfoils and high-quality aerodynamic and geometric labels, two novel and practical airfoil inverse design tasks, \emph{i.e.,} conditional generation on multimodal physical parameters, controllable editing, and comprehensive metrics to evaluate various existing airfoil inverse design methods. Our aim is to establish \emph{AFBench} as an ecosystem for training and evaluating airfoil inverse design methods, with a specific focus on data-driven controllable inverse design models by multimodal instructions capable of bridging the gap between ideas and execution, the academic research and industrial applications. We have provided baseline models, comprehensive experimental observations, and analysis to accelerate future research. Our baseline model is trained on an RTX 3090 GPU within 16 hours. The codebase, datasets and benchmarks will be available at \url{https://hitcslj.github.io/afbench/}.