Haiquan Li1, Qinghui Zhang2,*, Xiaoqian Chen3
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 605-621, 2021, DOI:10.32604/cmes.2021.015747
Abstract Flight load computations (FLC) are generally expensive and time-consuming. This paper studies deep learning (DL)-based surrogate models of FLC to provide a reliable basis for the strength design of aircraft structures. We mainly analyze the influence of Mach number, overload, angle of attack, elevator deflection, altitude, and other factors on the loads of key monitoring components, based on which input and output variables are set. The data used to train and validate the DL surrogate models are derived using aircraft flight load simulation results based on wind tunnel test data. According to the FLC features, a deep neural network (DNN)… More >
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