Design-Space Dimensionality Reduction Benchmark Dataset – RAE2822 Airfoil

This dataset provides a benchmark database for the evaluation and comparison of design-space dimensionality reduction methods in aerodynamic shape optimization problems. The dataset consists of 16,385 configurations of the RAE2822 airfoil geometry generated through Sobol sampling of a parametric model defined by the linear superposition of 20 basis functions. Each configuration is described by 20 design variables, which define perturbations of the reference airfoil geometry. The airfoil surface is uniformly discretized using 129 points along the profile. Each configuration includes: discretized airfoil geometry coordinates (129 × x and y points) normalized design variables defining the parametric model pressure coefficient distribution (Cp) evaluated at 129 surface points integrated aerodynamic quantities (Cl, Cd, Cm) Aerodynamic quantities were computed using the XFOIL solver. Simulation conditions: Mach number: 0.4 Reynolds number: 6.5 × 10⁶ angle of attack: 0° The dataset includes: database.mat (410 × 16385 matrix) metadata.txt (matrix structure) README.md (dataset documentation) All design variables are provided in normalized form within the interval [0,1], therefore no additional parameter range file is required. Dataset statistics: total configurations: 16385 valid simulations: 8516 solver failures: 7869 parametrization failures: 0 This dataset is part of a benchmark collection aimed at providing standardized databases for evaluating dimensionality reduction techniques in engineering design spaces. The parametrization and simulation setup are described in: Serani, A., Palma, G., Wackers, J., Quagliarella, D., Gaggero, S., & Diez, M. (2025). Extending parametric model embedding with physical information for design-space dimensionality reduction in shape optimization. Engineering with Computers, 1-21. Serani, A., Diez, M., & Quagliarella, D. (2024). Aerodynamic shape optimization in transonic conditions through parametric model embedding. Aerospace Science and Technology, 155, 109611.