基于学习算法的结构大变形预测及气动弹性分析

Large flexible aircraft possess large structural deformation with aerodynamic loads, which makes the dynamic characteristics change obviously. Structural deformation predictions are vital to large flexible aircraft design and aeroelastic simulations. Full-order models like the finite element method have low simulation efficiency. Traditional reduced-order model (ROM) obtains high simulation efficiency but requires large amounts of sample data participating in model building. This study investigates machine learning algorithms to build a prediction model to calculate the static deformation of flexible structures considering geometric nonlinearities. The performance of the prediction models is compared under evaluation with root mean square error (RMSE). It is shown that several machine learning techniques can be applied to the prediction of large deformations. Moreover, a new static aeroelastic analysis method is proposed with a large deformation prediction model and non-planar vortex lattice method (VLM) with high accuracy and efficiency In the end, a single-beam flexible wing model is used, and the prediction model and wind tunnel test's static aeroelastic response are contrasted. The results demonstrate that the proposed method has good performance and great practical application value.