基于多任务学习的翼型结冰与气动特性预测OA

To address the problem of limited accuracy in single-task learning for airfoil icing prediction and the difficulty in simultane-ously obtaining icing morphology and aerodynamic characteristics,a multi-task learning based airfoil icing prediction framework called MTAF(Multi-Task Airfoil Former)is proposed.The framework employs an encoder-decoder architecture that extracts deep representations of airfoil geometry and flight conditions through a shared fea-ture encoder,realizes feature fusion and task interaction via a task correlation module,and accomplishes icing predic-tion and aerodynamic coefficient prediction respectively through dedicated prediction heads.The performance of Transformer and ConvNeXt backbone net-works within this framework is comparatively studied.Based on training and testing with 625 NACA 2412 airfoil samples,results demonstrate that the Transformer architecture achieves pixel accu-racy of 99.79%and coefficient of determination for 0.990 6 in icing prediction tasks,significantly outperforming 99.50%and 0.977 2 of ConvNeXt.For aerodynamic coefficient prediction,Transformer achieves coefficient of deter-mination values of 0.975 4 for lift coefficient and 0.987 4 for drag coefficient,which are comparable to 0.973 3 and 0.990 1 of ConvNeXt,respectively,with each architecture showing distinct advantages.The MTAF framework main-tains stable prediction performance across different icing types and various flight conditions,providing a high-precision prediction tool for airfoil icing engineering applications.