神经场集成用于气动表面预测：ONERA CRM壁面分布2025挑战赛获胜方案

中文摘要 AI 辅助

机器学习代理模型为高保真计算流体动力学（CFD）模拟在气动分析和设计中提供了一种有前景的替代方案。然而，由于复杂几何形状、多种流动状态以及有限的训练数据，为真实飞机配置构建准确的代理模型仍然具有挑战性。本工作介绍了在ONERA CRM壁面分布回归挑战赛中获得第一名的 methodology，该挑战赛专注于在不同运行条件下预测NASA通用研究模型机翼-机身-挂架-发动机短舱配置上的压力系数和摩擦系数分布。所提出的方法将问题表述为一个条件神经场，将空间坐标、表面法向量和运行条件映射到气动壁面量。傅里叶特征编码、与挑战指标对齐的相对平方误差目标、集成学习和k折交叉验证被逐步引入，以提高预测精度并利用有限的训练数据。除了介绍最终方法外，本文还通过全面的消融研究记录了导致获胜方案的连续模型设计选择，并讨论了若干被研究但最终放弃的替代方法。在隐藏的竞赛测试集上，所提出的方法取得了8.81的总分，优于组织者提供的最强基线（得分为8.64），同时所需的可训练参数大约少三个数量级。这些结果表明，精心设计的基于坐标的神经场构成了在有限数据条件下复杂几何气动代理建模的高效且稳健的框架。

英文摘要

Machine-learning surrogate models offer a promising alternative to high-fidelity Computational Fluid Dynamics (CFD) simulations for aerodynamic analysis and design. However, constructing accurate surrogates for realistic aircraft configurations remain challenging due to complex geometries, multiple flow regimes, and limited training data. This work presents the methodology that achieved first place in the ONERA CRM Wall Distribution Regression Challenge, which focuses on predicting pressure and skin-friction coefficient distributions over the NASA Common Research Model wing-body-pylon-nacelle configuration under different operating conditions. The proposed approach formulates the problem as a conditional neural field mapping spatial coordinates, surface normals, and operating conditions to aerodynamic wall quantities. Fourier feature encoding, a relative squared error objective aligned with the challenge metric, ensemble learning, and $k$-fold cross-validation are progressively introduced to improve prediction accuracy and exploit the limited training data. Beyond presenting the final methodology, the paper documents the successive model design choices that led to the winning solution through a comprehensive ablation study and discusses several alternative approaches that were investigated but ultimately discarded. On the hidden competition test set, the proposed methodology achieves an overall score of 8.81, outperforming the strongest organizer-provided baseline, which achieved a score of 8.64, while requiring approximately three orders of magnitude fewer trainable parameters. These results illustrate that carefully designed coordinate-based neural fields constitute an efficient and robust framework for aerodynamic surrogate modeling on complex geometries under limited-data conditions.

发表机构

• Cenaero

机构由 AI 辅助整理，请以论文原文为准。