Abstract:  Aiming at the problems of complex and low-efficiency one-dimensional high-order model of thin-walled structures, a method of identifying cross-sectional deformation of thin-walled structures based on principal component analysis was proposed. First, a set of basis functions was constructed to capture the section deformation and one-dimensional high-order model of the thin-walled structure was established. The basis functions were integrated into the dynamic equations and decoupled by solving the related generalized eigenvalue problems. Then, the feature vector was decomposed based on principal component analysis to identify the axial change pattern of the basis function. Each feature vector was orthogonally decomposed into components corresponding to changing patterns, and weight matrix linear combinatorial basis functions were constructed. Finally, the initial dynamics equation was simplified with the identified deformation patterns. The results show that the identified deformation mode has the ability to reproduce the three-dimensional mode shape of thin-walled structures. Comparing with the ANSYS model, the first ten-order natural frequency error of the high-order model is within 1.94%, which provides a theoretical basis for improving the calculation efficiency of the dynamic model of thin-walled structures.

Key words:  thin wall structure; one-dimensional high-order model; principal component analysis(PCA); feature deformation recognition