Simrank-based prediction of crash simulation similarities

Abstract

Data searchability has been utilized for decades and is now a crucial ingredient of data reuse. However, data searchability in industrial engineering is essentially still at the level of individual text documents, while for finite element (FE) simulations no content-based relations between FE simulations exist so far. Additionally, the growth of data warehouses with the increase of computational power leaves companies with a vast amount of engineering data that is rarely reused. Search techniques for FE data, which are in particular aware of the engineering problem context, is a new research topic. We introduce the prediction of similarities between simulations using graph algorithms, which for example allows the identification of outliers or ranks simulations according to their similarities. With that, we address searchability for FE-based crash simulations in the automotive industry. Here, we use SimRank-based methods to predict the similarity of crash simulations using unweighted and weighted bipartite graphs. Motivated by requirements from the engineering application, we introduce SimRankTarget++ an alternative formulation of SimRank++ that performs better for FE simulations. To show the generality of the graph approach, we compare component-based similarities with part-based ones. For that, we introduce a method for automatically detecting components in the vehicle. We use a car sub-model to illustrate the similarity ansatz and present results on data from real-life development stages of an automotive company.