AirfoilMNIST: A Large-Scale Dataset based on Two-Dimensional RANS Simulations of Airfoils

Studying aerodynamic problems is still dominated by computationally heavy simulations which require substantial resources and knowledge about aerodynamics and numerical methods. While first adaptions of artificial intelligence have reached the field of aerodynamics to accelerate this process, data is still scarce and often lacks the proper validation. To bridge this gap, we propose airfoilMNIST, a comprehensive dataset of two-dimensional, RANS-based flow fields for NACA 4- and 5-series airfoils. Our dataset consists of around 150’000 samples for Mach numbers up to 0.6 and angles of attack −5 <= alpha <= 15 where each sample contains the mean flow fields for density, eddy viscosity, pressure, temperature, turbulent kinetic energy, turbulent thermal diffusivity, specific rate of dissipation and velocity.当前气动问题的研究仍以计算量庞大的数值仿真为主，此类仿真不仅需要耗费大量计算资源，还要求研究者具备扎实的气动学与数值方法相关知识。尽管人工智能的早期适配应用已进入气动领域以加速该研究流程，但相关数据仍较为匮乏，且往往缺乏有效的验证。为填补这一研究空白，我们提出了airfoilMNIST数据集——一套面向NACA 4系列与5系列翼型的、基于RANS（Reynolds-Averaged Navier-Stokes）的二维流场综合数据集。本数据集包含约15万个样本，其覆盖的马赫数最高可达0.6，攻角范围为−5 ≤ α ≤ 15；每个样本均包含密度、涡粘性、压强、温度、湍流动能、湍流热扩散率、比耗散率以及速度的平均流场数据。