发表机构
University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文以NLR 7301翼型为对象，采用LSTM神经网络，通过残差学习方法学习CFD升力与Wagner模型预测值的差值，其泛化能力优于直接预测CFD升力的神经网络，可用于增强经典低阶气动力理论。
AI 中文摘要
本文研究采用残差学习改进用于气动弹性应用的非定常气动力载荷预测的可行性。研究选用的机器学习技术是长短期记忆（LSTM）神经网络，因其适用于具有气动力记忆效应的序列数据。该方法在NLR 7301翼型基准上进行研究，采用跨音速流场中存在激波运动时，规定俯仰和 plunge 运动的高保真CFD升力数据。采用基于Wagner函数的解析非定常气动力模型作为基于物理的基线，训练神经网络以学习CFD升力系数与Wagner预测值之间的差值。将残差模型与直接预测CFD升力系数的神经网络模型进行比较，比较内容包括特征和归一化研究、外部基准案例，以及在一系列正弦和非正弦运动上的留一法和留族法泛化测试。当残差模型的输入与Wagner公式变量对齐时，其性能最佳，通常在训练运行中误差更低且性能更一致，不过直接模型在某些高频案例中仍更准确。残差模型在留一法和留族法测试中也表现出更好的泛化能力，当从训练中排除整个运动族时，误差的增幅比直接模型更小。总体而言，结果表明残差学习作为一种模块化方法，在增强经典低阶气动力理论方面具有潜力，尤其是当物理基线去除气动力响应的结构化部分，留下方差较低的修正项供神经网络学习时。
英文摘要
This paper investigates the feasibility of using residual learning to improve unsteady aerodynamic load prediction for aeroelastic applications. The machine learning technique selected for the study is the long short-term memory (LSTM) neural network, which is used for its suitability for sequential data with aerodynamic memory effects. The approach is investigated for the NLR 7301 airfoil benchmark using high-fidelity CFD lift data for prescribed pitch and plunge motions in the transonic flow regime in the presence of shock motion. An analytical unsteady aerodynamic model based on the Wagner function is used as a physics-based baseline, and the neural network is trained to learn the difference between the CFD lift coefficient and the Wagner prediction. The residual model is compared with a direct neural-network model trained to predict the CFD lift coefficient. The comparison includes feature and normalization studies, external benchmark cases, and leave-one-out and leave-family-out generalization tests across a range of sinusoidal and non-sinusoidal motions. The residual model performs best when its inputs align with the Wagner formulation variables, generally giving lower error and more consistent performance across training runs, though the direct model remains more accurate for some high-frequency cases. The residual model also generalizes better in the leave-one-out and leave-family-out tests, with a smaller increase in error than the direct model when entire motion families are withheld from training. Overall, the results indicate that residual learning shows promise as a modular approach for augmenting classical low-order aerodynamic theories, especially when the physics baseline removes a structured part of the aerodynamic response and leaves a lower-variance correction for the neural network to learn.
Comments27 pages, 21 figures, 9 Tables
