A Convolutional Neural Network-Based Stress Prediction Method for Airfoil Structures

As a vital component of an aircraft, the structural integrity of the wing is closely linked to both flight performance and safety, making it essential to accurately predict the stresses within its structure. However, conventional stress calculation methods often encounter significant computational costs and lengthy analysis times when addressing highly nonlinear and complex geometries. To address these challenges, this paper introduces a deep learning-based stress prediction approach called the Multi-scale Attention Enhanced Unet (MA-Unet) model. The model incorporates a multi-scale feature extraction and attention mechanism based on Unet to capture complex stress distribution features more efficiently, and is applied to the stress prediction of wing skin structures. A stress field dataset is generated through numerical simulation, which is then used to train and evaluate the MA-Unet model. The prediction results are compared with those obtained from traditional convolutional neural networks (CNNs) and the Unet model. Experimental results demonstrate that the MA-Unet model achieves higher accuracy in predicting wing skin stresses and shows strong robustness across various testing conditions. This model serves as an effective method and provides valuable data support for the rapid and accurate assessment of wing structures, highlighting its significant practical applications.