A new graph-based surrogate model for rapid prediction of crashworthiness performance of vehicle panel components

• URL: http://arxiv.org/abs/2503.17386v1
• Date: Sun, 16 Mar 2025 23:55:40 GMT
• Title: A new graph-based surrogate model for rapid prediction of crashworthiness performance of vehicle panel components
• Authors: Haoran Li, Yingxue Zhao, Haosu Zhou, Tobias Pfaff, Nan Li,
• Abstract summary: Graph Neural Networks (GNNs) have emerged as a promising solution for processing data with complex structures.<n>This paper proposes Recurrent Graph U-Net (ReGUNet), a new graph-based surrogate model for crashworthiness analysis of vehicle panel components.
• Score: 9.652891407528612
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: During the design cycle of safety critical vehicle components such as B-pillars, crashworthiness performance is a key metric for passenger protection assessment in vehicle accidents. Traditional finite element simulations for crashworthiness analysis involve complex modelling, leading to an increased computational demand. Although a few machine learning-based surrogate models have been developed for rapid predictions for crashworthiness analysis, they exhibit limitations in detailed representation of complex 3D components. Graph Neural Networks (GNNs) have emerged as a promising solution for processing data with complex structures. However, existing GNN models often lack sufficient accuracy and computational efficiency to meet industrial demands. This paper proposes Recurrent Graph U-Net (ReGUNet), a new graph-based surrogate model for crashworthiness analysis of vehicle panel components. ReGUNet adoptes a U-Net architecture with multiple graph downsampling and upsampling layers, which improves the model's computational efficiency and accuracy; the introduction of recurrence enhances the accuracy and stability of temporal predictions over multiple time steps. ReGUNet is evaluated through a case study of side crash testing of a B-pillar component with variation in geometric design. The trained model demonstrates great accuracy in predicting the dynamic behaviour of previously unseen component designs within a relative error of 0.74% for the maximum B-pillar intrusion. Compared to the baseline models, ReGUNet can reduce the averaged mean prediction error of the component's deformation by more than 51% with significant improvement in computational efficiency. Provided enhanced accuracy and efficiency, ReGUNet shows greater potential in accurate predictions of large and complex graphs compared to existing models.

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