基于残差神经网络的飞机力学响应预测方法

doi:10.7527/S1000-6893.2025.31295

Abstract

Abstract:

Digital twin technology for aircraft structures reflects the mechanical response and comprehensive performance of an aircraft’s lifecycle through high-fidelity and dynamically updated digital models. To improve the accuracy of structural response predictions， digital twin models often use multi-level， refined simulation techniques. However， this leads to a significant increase in model computation amount and cost， making it challenging to meet the real-time prediction needs for aircraft structural strength during flight. AI-based reduced-order prediction of structural strength is a key technology for real-time prediction of structural responses during flight. By combining high-order digital twin model simulation results with structural loads inferred from actual flight data such as flight parameters and strain， it is possible to rapidly and accurately predict the mechanical response of aircraft structures. This addresses the timeliness issue of aircraft structural performance prediction during actual flights， and has gained increasing attention in the field of aircraft digital twin technology. This paper proposes a method for pixelating structural cloud maps and load inputs to express spatial relationships between point cloud data. To construct an intelligent prediction method for mechanical responses Based on load inputs， a convolutional neural network with cross-layer connection mechanisms is also introduced. Results from numerical experiments on the wing structure under 329 conditions show that the pixelation method can retain structural response characteristics， while making the data compatible with pixel-based data processing methods like convolution. Compared to traditional convolutional neural networks， the proposed residual neural network model achieves improved prediction accuracy and reduced loss. Additionally， this intelligent method achieves more than two orders of magnitude efficiency improvement compared to traditional simulations. The predicted stress distribution shows a high similarity to finite element simulation stress distributions， highlighting its application value in the digital twin of aircraft structures.

Key words: digital twin, residual neural network, convolutional neural network, intelligent prediction, real-time prediction

CLC Number:

Yinxuan ZHANG, Qi ZHANG, Zhenyong XU, Linshu MENG. Predicting method of aircraft mechanical response based on residual neural networks[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531295.

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预测结构	设计模型训练损失/10^-5	设计模型预测损失/10^-5	对比模型训练损失/10^-5	对比模型预测损失/10^-5
上翼面	2.61	3.01	5.40	6.970
下翼面	6.78	8.77	5.17	0.104
肋	2.87	3.75	5.32	5.300
梁	4.48	5.32	7.47	8.350

预测结构	设计模型最大误差	设计模型平均误差/10^-3	对比模型最大误差	对比模型平均误差/10^-3
上翼面	0.180	2.25	0.408	3.28
下翼面	0.209	3.66	0.229	4.27
肋	0.174	2.38	0.214	4.31
梁	0.149	2.87	0.210	3.26

预测方法	单次总时长/s	单次净时长/s
传统有限元仿真	41.8	37.2
残差神经网络模型	2.9	0.4

Predicting method of aircraft mechanical response based on residual neural networks

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