构建999种飞翼布局飞机数据集，实现高精度气动预测。
BlendedNet: A Blended Wing Body Aircraft Dataset and Surrogate Model for Aerodynamic Predictions
- 用点云+FiLM网络，从表面数据反推几何与飞行状态
- 覆盖8830个工况，每例约1000万网格，预测误差低
- 适合气动设计、数据驱动建模研究者使用
BlendedNet 是一个公开的飞翼布局（BWB）飞机气动数据集，包含999种几何构型，每种在约九种飞行条件下进行仿真，共生成8830个收敛的RANS计算案例，每个案例包含9至1400万网格单元。数据通过采样几何参数和飞行条件生成，包含用于研究升力与阻力的详细表面点数据。我们还提出一种端到端的代理建模框架：先用不变排列的PointNet回归器从采样表面点云中预测几何参数，再将预测结果与飞行条件共同作用于特征调制（FiLM）网络，以预测点位系数 Cp、Cfx、Cfz。实验表明，在多种飞翼布局下均能实现低误差的表面预测。该数据集缓解了非传统构型的数据稀缺问题，推动数据驱动代理模型在气动设计中的应用。
原文摘要 · Abstract (English)
BlendedNet is a publicly available aerodynamic dataset of 999 blended wing body (BWB) geometries. Each geometry is simulated across about nine flight conditions, yielding 8830 converged RANS cases with the Spalart-Allmaras model and 9 to 14 million cells per case. The dataset is generated by sampling geometric design parameters and flight conditions, and includes detailed pointwise surface quantities needed to study lift and drag. We also introduce an end-to-end surrogate framework for pointwise aerodynamic prediction. The pipeline first uses a permutation-invariant PointNet regressor to predict geometric parameters from sampled surface point clouds, then conditions a Feature-wise Linear Modulation (FiLM) network on the predicted parameters and flight conditions to predict pointwise coefficients Cp, Cfx, and Cfz. Experiments show low errors in surface predictions across diverse BWBs. BlendedNet addresses data scarcity for unconventional configurations and enables research on data-driven surrogate modeling for aerodynamic design.
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