CRADIPOR: Crash Dispersion Predictor

TL;DR

CRADIPOR is a novel numerical tool that predicts dispersion in automotive crash simulations using autoencoders and classification, enhancing post-processing analysis without rerunning simulations.

Contribution

Introduces a RRAE-based prediction framework for identifying dispersion-sensitive regions in crash models, outperforming baseline methods.

Findings

RRAE framework outperforms Random Forest in detection accuracy.

Wavelet and slope-based inputs are most effective for classification.

Slope variations yield the best performance in identifying dispersion-sensitive regions.

Abstract

We present CRADIPOR, a numerical dispersion prediction tool for automotive crash simulations. Finite Element (FE) crash models are widely used throughout vehicle development, but their predictions are not strictly repeatable because of parallel computation and model complexity. As a result, performance criteria evaluated during post-processing may exhibit significant numerical dispersion, which complicates engineering decision-making. Although dispersion can be estimated by repeating the same simulation, this approach is generally impractical because of its high computational cost. This work therefore investigates a prediction tool that can be applied during routine crash-simulation post-processing without repeating the computation. The proposed approach relies on a Rank Reduction Autoencoder (RRAE) combined with supervised classification in order to identify regions sensitive to numerical dispersion. The comparative analysis suggests that the RRAE-based framework is more effective than the Random Forest baseline on the studied dataset. Among the tested signal representations, wavelet-based and slope-based inputs appear to be the most promising, with slope variations providing the best classification performance. These results support the use of structured latent representations for improving numerical-dispersion detection in automotive crash post-processing.