关键词: 数字孪生, 残差神经网络, 卷积神经网络, 智能预测, 实时预测

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