CRADIPOR: Crash Dispersion Predictor

2026-04-30

AI Summary

This study addresses the numerical dispersion inherent in automotive crash simulations—arising from parallel computation and model complexity—which undermines engineering decision-making, while conventional approaches relying on repeated simulations incur prohibitive computational costs. To overcome this, the authors propose a novel post-processing framework that integrates a Reduced-Rank Autoencoder (RRAE) with supervised classification to efficiently identify regions sensitive to numerical dispersion without requiring additional simulations. By leveraging structured latent representations and signal features such as slope variations, the method substantially enhances the detection of dispersion-sensitive areas. Experimental results demonstrate that the proposed framework outperforms a random forest baseline on the test set, confirming its effectiveness and practicality for stability assessment in crash simulation post-processing.

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.

Problem

• numerical dispersion
• crash simulation
• finite element
• post-processing
• repeatability

Innovation

• numerical dispersion
• Rank Reduction Autoencoder
• crash simulation
• latent representation
• supervised classification