Conceptual Crash Analysis in Automotive Applications with Reduced Computational Effort

In the realm of automotive safety engineering, the demand for efficient and accurate crash simulations is ever-increasing. As finite element (FE) modeling of components becomes increasingly detailed and the availability of advanced material models improves, crash simulations for full vehicles can become time-consuming. Evaluating the crash performance of any vehicle subsystem requires full vehicle simulations at the industry level, which are complex and time-consuming. This paper introduces a novel methodology for replicating full vehicle crash simulations using Principal Component Test (PCT) cut-model level. Adapting PCT simulations, a significant reduction in computational time and resource usage is achieved, thereby optimizing CPU cluster performance. The proposed method has been successfully implemented across various crash load cases, including frontal 100 percent overlap, 40 percent overlap, small overlap crash, side impact, rear impact, underbody impact and low speed impact scenarios. The results from these PCT simulations exhibit strong correlation with full vehicle simulations, ensuring reliability and validity. This approach not only enhances simulation efficiency, cluster utilization cost but also offers a scalable solution for future automotive crash evaluation and optimization. The findings underscore the potential for widespread application of Principal Component Test (PCT) cut-model in the industry, paving the way for more sustainable and cost-effective crash simulation practices.