This conference paper was submitted for presentation at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

In order to achieve and fulfill design and crash criteria as a central component of a vehicle development project, the adaptation and redesign of the simulation model is the central task of the engineer. These changes are validated by new simulation runs, which ideally show improved behavior. The challenge here is, on the one hand, to analyze the changes and, on the other hand, to exclude the possibility that the changes have undesired side effects. These effects can either be known but undesirable, or unknown and therefore not yet evaluated in terms of their impact on crash behavior. In both cases, it is the engineer's task to find and document all the effects of the applied changes and, if necessary, adapt the model accordingly. Comparing two or more simulation results is time-consuming. This process can be automated using machine learning. For this purpose, a database is created, for example for a construction kit or a development tree. This database is expanded with each analysis if new crash behavior in terms of deformation or the behavior of a post variable is detected. All behaviors contained in the database thus represent the event horizon against which each new simulation is compared. If a behavior is known but unintentional, this behavior can be tracked. This means that the user is warned for every simulation that exhibits this behavior. The manageability of the database depends heavily on its size. Thanks to data compression techniques and the reduction to the essential components of a crash result, the database only requires a fraction of the original simulation results. For a use case in which the sum of all simulation results amounted to more than 7 TB, the database is only 14 GB, which corresponds to a reduction by a factor of over 500. But what if, for example, you have seen a certain behavior of a component in a test, but this does not match the final simulation result? Or you are interested to know whether a certain crash behavior occurred in the set of all simulations. In this case, a geometric search can be carried out in the database. By applying Model Order Reduction (MOR) the new deformation pattern is projected to the low dimensional latent space of the simulation results available in the database. In the low dimensional space the distances between the deformation pattern of interest and all deformation patters of all simulations results for all time steps are analyzed and a similarity score is determined. This provides an overview of which models and at which time steps the behavior was similar. It is therefore possible to search for early buckling behavior as well as for the final state of a component deformation. Even for data bases containing several hundreds of full vehicle crashes, the time of search is just a few seconds making it an interactive task.