Accelerate Ped-Pro assessments using SimAI

Deep learning methods have had a significant impact on design process in the recent past. SimAI is a deep learning-based AI platform that has shown to be very effective in approximating the behavior of fluid flow applications, especially fully developed steady state flows simulated by CFD solvers. The underlying neural networks in SimAI are very versatile and can be easily extended to structural applications as well. This study aims at demonstrating the applicability of SimAI for non-linear transient structural simulations like pedestrian protection. We start with a simple Tube Crush model to demonstrate the use of SimAI to predict the deformed shape of the Tube at any time instance. We then train a model on different Tube shapes to show SimAI’s ability to learn from non-parametric geometry. Finaly, we demonstrate how SimAI can be used to accelerate Ped-pro evaluations. The NCAC Accord model is used to generate 96 training points. This dataset is used to train a SimAI model and the resulting trained model can predict the full field hood deformation as well as the HIC value for the corresponding hit location within 10% relative error on any point on the vehicle hood. SimAI is many orders of magnitude faster in predicting the HIC than direct numerical simulation and hence can be very effective in evaluating designs upfront in the vehicle development process.