G2Aero Database of Airfoils - Curated Airfoils

This dataset contains a curated set of 19,164 airfoil shapes from various applications and the data-driven design space of separable shape tensors (PGA space), which can be used as a parameter space for machine-learning applications focused on airfoil shapes. We constructed the airfoil dataset in two main stages. First, we identified 13 baseline airfoils from the NREL 5MW and IEA 15MW reference wind turbines. We reparameterized these shapes using least-squares fits of 8-order CST parametrizations, which involve 18 coefficients. By uniformly perturbing all 18 CST coefficients by +/-20% around each baseline airfoil, we generated 1,000 unique airfoils. Each airfoil was sampled with 1,001 shape landmarks whose x-coordinates followed a cosine distribution along the chord. This process resulted in a total of 13,000 airfoil shapes, each with 1,001 landmarks. In the second phase, we gathered additional airfoils from the extensive BigFoil database, which consolidates data from sources such as the University of Illinois Urbana-Champaign (UIUC) airfoil database, the JavaFoil database, the NACA-TR-824 database, and others. We undertook a thorough pre-processing step to filter out shapes with sparse, noisy, or incomplete data. We also removed airfoils with sharp leading edge and those exceeding our threshold for trailing edge thickness. Additionally, we thinned out the collection of NACA airfoils-- parametric sweeps of NACA airfoils with increasing thickness and camber present in BigFoil database-- by selecting every fourth step in the parameter sweeps. Finally, we regularized the airfoils by reparametrizing them with an 8-order CST parametrization (with 1,001 shape landmarks with x coordinated following cosine distribution along the chord) and removing airfoils with high reconstruction errors. This data pre-processing resulted in a set of 6,164 airfoils. In total, our curated airfoil dataset comprises 19,164 airfoils, each with 1,001 landmarks, and is stored in the curated_airfoils.npz file. Using this curated airfoil dataset, we utilized the separable shape tensors framework to develop a data-driven parameterization of airfoils based on principal geodesic analysis (PGA) of separable shape tensors. This PGA space is provided in PGAspace.npz file.

本数据集包含经精心甄选整理的19164个翼型形状，覆盖多类应用场景，以及可分离形状张量（separable shape tensors）的数据驱动设计空间（即PGA空间），该空间可作为面向翼型形状的机器学习应用的参数空间。我们分两大阶段构建该翼型数据集。第一阶段，从NREL 5MW与IEA 15MW参考风力涡轮机中提取13款基准翼型。我们采用8阶CST参数化（CST parametrization，含18个系数）的最小二乘拟合对这些翼型形状进行重参数化。以每款基准翼型的18个CST系数为中心，在±20%的范围内进行均匀扰动，由此生成1000个独特翼型。每个翼型通过1001个形状地标点进行采样，其x坐标沿弦长呈余弦分布。该流程共计生成13000个翼型形状，每个翼型均配有1001个地标点。第二阶段中，我们从大型BigFoil数据库中收集额外翼型，该数据库整合了来自伊利诺伊大学厄巴纳-香槟分校（University of Illinois Urbana-Champaign, UIUC）翼型数据库、JavaFoil数据库、NACA-TR-824数据库等多方来源的数据。我们开展了全面的预处理流程，以过滤掉数据稀疏、含噪或不完整的翼型。同时移除前缘尖锐以及后缘厚度超出阈值的翼型。此外，针对BigFoil数据库中收录的NACA翼型（即厚度与弯度递增的参数扫描NACA翼型）集合，我们进行了精简：在参数扫描过程中每间隔4个步骤选取一个翼型。最终，我们通过8阶CST参数化（采用1001个沿弦长呈余弦分布的x坐标地标点）对剩余翼型进行重参数化，并移除重构误差较高的翼型，完成翼型的正则化处理。经此数据预处理流程，得到6164个翼型。综上，本经精心甄选整理的翼型数据集共计包含19164个翼型，每个翼型均配有1001个地标点，存储于curated_airfoils.npz文件中。依托该精心整理的翼型数据集，我们采用可分离形状张量框架，基于可分离形状张量的主测地线分析（Principal Geodesic Analysis, PGA）构建了翼型的数据驱动参数化方法。该PGA空间存储于PGAspace.npz文件中。

搜集汇总

背景与挑战

背景概述

该数据集是一个精心整理的翼型形状集合，包含19,164个翼型，每个翼型由1,001个地标点描述，数据来源于基准翼型扰动和BigFoil数据库的筛选预处理。它提供了基于可分离形状张量的主测地线分析（PGA）参数空间，适用于机器学习应用中的翼型形状建模和设计。