Dataset for the paper "Multi-fidelity transonic aerodynamic loads estimation using Bayesian neural networks with transfer learning"

This repository provides the multi-fidelity aerodynamic datasets used in:A. Vaiuso, G. Immordino, M. Righi, A. Da Ronch. "Multi-fidelity transonic aerodynamic loads estimation using Bayesian neural networks with transfer learning." Aerospace Science and Technology, 163 (2025) 110301.https://doi.org/10.1016/j.ast.2025.110301 The datasets enable training, validation, and benchmarking of a surrogate model for transonic aerodynamic load prediction with quantified uncertainty. They include low-, mid-, and high-fidelity aerodynamic data for a finite wing test case in transonic conditions and a full eVTOL aircraft configuration. 1. Finite Wing – Benchmark Supercritical Wing (BSCW) Design space: Mach number ∈ [0.70 , 0.84] and Angle of Attach AoA ∈ [0, 4] deg. Low-fidelity (LF): 625 samples generated with XFoil (panel method with 3D corrections). Mid-fidelity (MF): 49 RANS simulations with SU2 (coarse 2.5M-cell hybrid grid, Spalart–Allmaras turbulence model). Grid Convergence Index ≈ 5.4%. High-fidelity (HF): 58 RANS simulations with SU2 (fine 15.6M-cell grid, GCI ≈ 0.6%). Of these, 7 samples were used for model fine-tuning, while 51 were retained as an independent HF test set. Outputs: lift coefficient (CL) and pitching moment coefficient (CM) relative to 30% chord. 2. Full–Configuration eVTOL Vehicle Geometry: 5-propeller configuration with four symmetric lift propellers (front/rear) and one tail-mounted pusher. Design space: AoA ∈ [−180 , 180] deg, freestream velocity U ∈ [0 , 40] m/s, and propeller RPM up to ±4000. Low-fidelity (LF): 2000 samples from Blade Element Momentum (BEM) methods with momentum-theory-based induced velocity corrections. Mid-fidelity (MF): 250 simulations using the DUST framework (Vortex Particle Method with nonlinear lifting-line). Between 30–40 lifting lines per blade; wing discretised with ~3000 panels per side. Outputs: thrust (KT) and torque (KQ) coefficients for each of the five propellers. PurposeThese datasets support the development of multi-fidelity machine-learning frameworks for transonic aerodynamics, providing training material for uncertainty-aware surrogate models. They are also relevant for applications in aeroelasticity, flight dynamics, and design of unconventional aircraft (e.g. eVTOLs). Contents Raw aerodynamic datasets at LF, MF, HF fidelities. Partitioning into training, validation, and independent test sets as described in the paper. Associated aerodynamic coefficients across Mach–AoA (BSCW) and AoA–velocity–RPM (eVTOL) parameter spaces. KeywordsMulti-fidelity modelling; Bayesian neural networks; transfer learning; uncertainty quantification; Benchmark Supercritical Wing; eVTOL aerodynamics; CFD; reduced-order modelling.本仓库提供了用于以下研究的多保真度气动数据集：A. Vaiuso、G. Immordino、M. Righi、A. Da Ronch. 《基于带迁移学习的贝叶斯神经网络的跨声速气动载荷估算》，《航空航天科学与技术》，163 (2025) 110301。https://doi.org/10.1016/j.ast.2025.110301 本数据集可用于训练、验证及对标跨声速气动载荷预测代理模型，并可量化其不确定性。数据集包含跨声速条件下有限翼展机翼测试算例以及全电动垂直起降（eVTOL）飞行器构型的低、中、高保真度气动数据。 1. 有限翼展机翼——基准超临界翼（Benchmark Supercritical Wing, BSCW） 设计空间：马赫数范围为[0.70, 0.84]，攻角（Angle of Attack, AoA）范围为[0, 4] 度。 低保真度（Low-fidelity, LF）：625个样本，由XFoil（带三维修正的面元法）生成。 中保真度（Mid-fidelity, MF）：49次SU2软件的雷诺平均纳维-斯托克斯（Reynolds-Averaged Navier–Stokes, RANS）仿真，采用250万单元的粗混合网格，Spalart–Allmaras湍流模型，网格收敛指数（Grid Convergence Index, GCI）约为5.4%。 高保真度（High-fidelity, HF）：58次SU2软件的RANS仿真，采用1560万单元的精细网格，GCI约为0.6%。其中7个样本用于模型微调，剩余51个作为独立的高保真度测试集。 输出量：相对于30%弦长的升力系数（CL）和俯仰力矩系数（CM）。 2. 全构型电动垂直起降飞行器（eVTOL） 几何构型：5桨叶布局，包含4个对称布置的升力桨（前/后）以及1个安装在尾翼的推进桨。 设计空间：攻角范围为[-180, 180] 度，来流速度U范围为[0, 40] m/s，螺旋桨转速最高为±4000 转/分钟。 低保真度（LF）：2000个样本，基于叶素动量（Blade Element Momentum, BEM）方法，结合基于动量理论的诱导速度修正得到。 中保真度（MF）：250次基于DUST框架的仿真，采用带非线性升力线的涡粒子方法，每片桨叶设置30~40条升力线，机翼每侧离散为约3000个面元。 输出量：5个螺旋桨各自的推力系数（KT）和扭矩系数（KQ）。 研究用途 本数据集可支撑跨声速气动领域多保真度机器学习框架的开发，为考虑不确定性的代理模型提供训练数据，同时也可应用于气动弹性、飞行动力学以及非常规飞行器（如电动垂直起降飞行器）的设计相关研究。 数据集内容 包含低、中、高保真度的原始气动数据； 按照论文所述划分为训练集、验证集以及独立测试集； 涵盖马赫数-攻角（BSCW）以及攻角-来流速度-桨叶转速（eVTOL）参数空间下的相关气动系数。 关键词 多保真度建模；贝叶斯神经网络；迁移学习；不确定性量化；基准超临界翼；电动垂直起降飞行器气动特性；计算流体动力学（Computational Fluid Dynamics, CFD）；降阶建模。