用物理信息机器学习加速机翼气动分析，速度比传统工具快1000倍
NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
- 融合物理规律与深度学习，构建可微分的机翼气动模型
- 在宽泛工况下误差低于2.0%，速度提升最高达1000倍
- 适合需要快速优化设计的航空航天工程师
NeuralFoil 是一个基于 Python 的开源工具，用于快速分析机翼气动特性，功能类似 XFoil。在保持相当精度的前提下，计算速度相比 XFoil 提升 8 至 1000 倍。该工具可在广泛输入空间中计算全局与局部气动参数：包括 18 维机翼形状空间（含控制面偏转）、360 度迎角、雷诺数范围从 $10^2$ 到 $10^{10}$、亚音速至跨音速阻力发散区域，以及不同湍流参数。结果与 XFoil 接近：简单算例平均阻力相对误差为 0.37%，复杂测试集（含大量失速与转捩情况）误差低至 2.0%。由于解具有 $C^ fty$ 光滑性、兼容自动微分且计算成本可控无发散问题，支持梯度驱动的设计优化。NeuralFoil 结合物理信息机器学习与解析模型，通过结构嵌入对称性、领域知识特征工程及已知极限情形的保证外推实现物理一致性。本工作还提出一种新的代理模型不确定性量化方法，支持鲁棒设计优化。通过多个案例研究，包括同时考虑气动与非气动约束的实际机翼优化，结果表明：仅需数秒即可生成性能与形状接近专家设计的机翼，为后续人工优化提供良好起点。
原文摘要 · Abstract (English)
NeuralFoil is an open-source Python-based tool for rapid aerodynamics analysis of airfoils, similar in purpose to XFoil. Speedups ranging from 8x to 1,000x over XFoil are demonstrated, after controlling for equivalent accuracy. NeuralFoil computes both global and local quantities (lift, drag, velocity distribution, etc.) over a broad input space, including: an 18-dimensional space of airfoil shapes, possibly including control deflections; a 360 degree range of angles of attack; Reynolds numbers from $10^2$ to $10^{10}$; subsonic flows up to the transonic drag rise; and with varying turbulence parameters. Results match those of XFoil closely: the mean relative error of drag is 0.37% on simple cases, and remains as low as 2.0% on a test dataset with numerous post-stall and transitional cases. NeuralFoil facilitates gradient-based design optimization, due to its $C^\infty$-continuous solutions, automatic-differentiation-compatibility, and bounded computational cost without non-convergence issues. NeuralFoil is a hybrid of physics-informed machine learning techniques and analytical models. Here, physics information includes symmetries that are structurally embedded into the model architecture, feature engineering using domain knowledge, and guaranteed extrapolation to known limit cases. This work also introduces a new approach for surrogate model uncertainty quantification that enables robust design optimization. This work discusses the methodology and performance of NeuralFoil with several case studies, including a practical airfoil design optimization study including both aerodynamic and non-aerodynamic constraints. Here, NeuralFoil optimization is able to produce airfoils nearly identical in performance and shape to expert-designed airfoils within seconds; these computationally-optimized airfoils provide a useful starting point for further expert refinement.
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