Spatiotemporal modeling based on manifold learning for collision dynamic prediction of thin-walled structures under oblique load

Abstract

Numerical simulation of the collision dynamics in thin-walled structures under oblique load involves complex spatiotemporal processes, including material, geometric, and contact nonlinearities, which often require significant computational resources and time. Moreover, predicting high-dimensional spatiotemporal responses remains a challenge for most surrogate-based models. This paper proposes a deep learning framework based on manifold learning for spatiotemporal modeling of collision dynamics in thin-walled structures under oblique load. The framework leverages multiple deep learning models, including Variational Autoencoders (VAE), Radial Basis Function Interpolation (RBFI), and regression Residual Network (ResNet18), to capture the complex nonlinearities inherent in structural deformation, stress distribution, and crush force, enabling continuous prediction of multimodal spatiotemporal responses. Using a rectangular thin-walled tube under oblique load as an example, the models are validated with simulation data, yielding average prediction errors of 5.80 % for structural deformation, 6.01 % for Energy Absorption (EA), 10.66 % for Peak Crush Force (PCF), and 16.66 % for crush force. Compared to traditional finite element (FE) simulations, prediction time is reduced by 98.6 % for structural deformation and stress distribution, and 97.4 % for crush force. Additionally, the method demonstrates stability and broad applicability across different design parameters and structural configurations, including rectangular and double-cell tubes. This work underscores the potential of deep learning techniques to enhance computational efficiency and predictive accuracy in the crashworthiness design of thin-walled structures.