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Analysis of Numerical Crash Simulation Data Using Dimensionality Reduction and Machine Learning

Abstract

With the increasing virtualization of automotive R&D processes, analyzing the growing amounts of numerical simulation data produced is becoming more and more challenging. A considerable amount of resources are spent to extract underlying knowledge about the crash behavior under certain input parameters. This research proposes data science methods to semiautomatically analyze numerical simulation data. The goals of this research include comparing different dimensionality reduction algorithms to represent simulation data as lower-dimensional embeddings, clustering algorithms to cluster simulations displaying similar crash behavioral patterns, finding causes for a certain behavioral pattern, and discovering design rules to avoid undesired behavioral patterns. The lightweight lower-dimensional embedding of the simulations is represented using feature extraction dimensionality reduction methods like Principal Component Analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). The simulations’ underlying knowledge is extracted from the lower-dimensional embedding and similar simulations are clustered based on their behavioral patterns by using unsupervised clustering algorithms like k-means, Balanced Iterative Reducing and clustering using Hierarchies (BIRCH). These behavioral patterns can be analyzed and the importance of the input parameters causing a certain behavioral pattern is obtained using random forests. Finally, the design rules to avoid an undesired behavioral pattern are extracted using decision tree algorithms and associative rule mining. The workflow is applied to a simple side pole impact simulation model, where a generic vehicle floor structure is impacted by a pole. To create a dataset, extensive simulations are carried out by varying the values of the input parameters using a Latin-Hypercube DoE scheme. Best results were obtained using a combination of a linear dimensionality reduction algorithm and a hybrid clustering algorithm, which yielded three different behavioral patterns containing 90 % of the original information respresented in 50 dimensions. The obtained results from rule extraction confirm the rules anticipated by domain experts to avoid a non-desired cluster. These design rules can assist engineers in efficiently defining successful future designs.

Document Details

ReferenceNWC21-154
AuthorBallal. N
LanguageEnglish
TypePaper
Date 27th October 2021
OrganisationFraunhofer EMI
RegionGlobal

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