Application of Macro Element Method (MEM) for faster automotive crash safety design during concept stage

Abstract

The non-linear nature of vehicle crash safety simulations using the Finite Element Method (FEM) usually requires a significant investment in computational time and resources, in addition to the requirement of full CAD (Computer Aided Design) data availability, which is challenging to obtain during the concept stage. The current work explores the usage of Macro Element Method (MEM) based on simplified Super Beam Element (SBE), for which the shape functions are developed based on experimental observations rather than algebraic equations. MEM crash analysis run time doesn’t exceed several seconds and is one of the underutilised analytical models which can be used to quickly explore new design directions during the early concept stage of the vehicle and has higher potential to speed up the product development process. In this paper, initially, a calibration study was done to validate the MEM at a component level to compare the results with FEM and experimental values. FEM and MEM simulation models were developed and evaluated for a metallic thin-walled square frusta with varying semi-apical angles subjected to axial compression. The results were compared, and it was found that MEM results show more than 97% correlation compared to FEM and experimental results. Subsequently, a design modification was introduced in the component and the results show close degree of correlation between MEM and FEM. Similar process was followed for full vehicle level simulations, such as full width frontal crash, side pole crash analyses to understand the robustness of the method in applying it to product development environment.

Keywords:

• FEM
• macro element method (MEM)
• crashworthiness
• machine learning
• super folding element (SFE)
• super beam element (SBE)

Acknowledgements

The first author would like to thank Dr. Akella Sarma for his invaluable motivation and guidance provided during this work. The first author thanks Mr. Ameet Hangarge and Mr. Arjun for their continuous support provided during the work.

Disclosure statement

No potential conflict of interest was reported by the author(s).