Airfoil Computational Fluid Dynamics - 9k shapes, 2 AoA's

This dataset contains aerodynamic quantities - including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) - for 8,996 airfoil shapes, computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes were designed using the separable shape tensor parameterization that encodes two-dimensional shapes as elements of the Grassmann manifold. This data-driven approach learns two independent spaces of parameter from a collection of sample airfoils. The first captures large-scale, linear perturbations, and the second defines small-scale, higher-order perturbations. For this data, we used the G2Aero database of over 19,000 airfoil shapes to learn a parameter space that captured a wide array of shape characteristics. We fixed the linear deformations to be the mean over the database and sampled new shapes over a four-dimensional parameter space of higher-order perturbation. This sampling approaches allows for isolated analysis of non-linear airfoil shape deformations while holding other aspects (e.g., airfoil thickness) approximately constant. The aerodynamic quantities for the generated airfoil were obtained using the HAM2D code, which is a finite-volume Reynolds-averaged Navier-Stokes (RANS) flow solver. We employ a fifth-order WENO scheme for spatial reconstruction with Roe's flux difference scheme for inviscid flux and second-order central differencing for viscous flux. A preconditioned GMRES method is applied for implicit integration. The Spalart-Allmaras 1-eq turbulence model is used for the turbulence closure, and the Medida-Baeder 2-eq transition model is applied to account for the effects of laminar turbulent transition. The airfoil grid is generated with a total of 400 points on the airfoil surface, the initial wall-normal spacing of y+ = 1, and an outer boundary located at 300 chord lengths away from the wall. The CFD simulations are performed at a freestream Mach number of 0.1, Reynolds number of 9M, and at two angles of attack, 4 deg. and 12 deg. The simulations were performed using the Bridges-2 system at the Pittsburgh Supercomputing Center in February 2023 as part of the INTEGRATE project funded by the Advanced Research Projects Agency - Energy in the U.S. Department of Energy. The data was collected, reformatted, and preprocessed for this OEDI submission in July 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub Repository resource under explore_airfoil_9k_data.ipynb.

本数据集包含8996种翼型的气动参数，涵盖流场参数（动量、能量与涡量）及汇总参数（升力、阻力与动量系数），所有参数均通过HAM2D计算流体动力学（CFD）模型计算得到。
翼型的生成采用可分离形状张量参数化方法，该方法将二维翼型编码为格拉斯曼流形（Grassmann manifold）上的元素。该数据驱动方法从样本翼型集合中学习两个独立的参数空间：第一个空间捕捉大规模线性扰动，第二个空间定义小规模高阶扰动。
本次数据集构建时，我们使用包含超19000种翼型的G2Aero数据库学习涵盖多样翼型特征的参数空间：将线性变形固定为数据库的均值，并在高阶扰动的四维参数空间中采样生成新翼型。该采样方法可在保持翼型厚度等其他属性近似恒定的前提下，实现非线性翼型变形的孤立分析。
生成翼型的气动参数通过HAM2D程序获取，该程序为有限体积法雷诺平均纳维-斯托克斯（RANS）流求解器。空间重构采用五阶加权本质无振荡（WENO）格式，无粘通量计算使用罗（Roe）通量差分格式，粘性通量则采用二阶中心差分格式。隐式积分采用预处理广义最小残差（GMRES）方法。湍流闭合选用Spalart-Allmaras单方程湍流模型，层流-湍流转捩效应的模拟则采用Medida-Baeder双方程转捩模型。
翼型网格的生成设置如下：翼型表面共布置400个网格点，初始法向壁面间距y+ = 1，外边界设置在距壁面300倍弦长的位置。
CFD模拟的来流马赫数为0.1，雷诺数为900万，攻角分别设置为4度与12度。
本模拟于2023年2月在美国匹兹堡超级计算中心的Bridges-2超算系统上完成，属于美国能源部高级研究计划局-能源（Advanced Research Projects Agency - Energy, ARPA-E）资助的INTEGRATE项目的一部分。2023年7月，在美国能源部风能技术办公室资助的“风能基础人工智能”项目框架下，我们完成了本数据集的收集、格式重构与预处理，并提交至开放能源数据集成（OEDI）平台。
本数据集旨在作为基准测试集，用于检验新型人工智能（AI）或机器学习（ML）工具的性能。本数据集的基线AI/ML分析方法已完成实现，相关模型的代码仓库链接已同步提供。.h5格式数据文件的结构可在GitHub仓库的"explore_airfoil_9k_data.ipynb"文件中查看。