Prediction of airbag behavior using tensor decomposition

A study conducted by Takashi Sasagawa and Masato Tanaka was published in the Scientific Reports.

We present a construction method for reduced-order models (ROMs) to explore alternatives to numerical simulation. The proposed method can efficiently construct ROMs for non-linear problems with contact and impact behaviors by using tensor decomposition for factorizing multidimensional data and Akima-spline interpolation without tuning any parameters. The performance of the proposed method is studied by constructing ROMs for airbag impact simulations based on limited learning data. The proposed ROMs can accurately predict airbag deployment behavior even for new parameter sets using the Akima-spline interpolation scheme. Furthermore, an extremely high data compression ratio (more than 1,000) and efficient predictions of the response surfaces and Pareto frontier (2,000 times faster than that of full finite element analyses using all parameter sets) can be realized.

Title: Construction of a Reduced-order Model Based on Tensor Decomposition and its Application to Airbag Deployment Simulations
Authors: Sasagawa, T., Tanaka, M.
Journal Name: Scientific Reports
Published: July 11, 2023
https://doi.org/10.1038/s41598-023-38393-2