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Automatic Outlier Detection for Crash Simulation Results


Abstract


To cope up with the ever growing amount of simulation runs being performed, tools and techniques are needed to make use of the huge amount of simulation data being stored. While current Simulation Data Management systems and modern IT infrastructure already allows to store and access huge datasets and would facilitate putting this into action for analysis, the user usually only has tools and the time to make rather straight forward model to model comparisons between current model versions and their immediate predecessors. To take analysis capabilities and model development a leap forward it is necessary to also make use of whole model development branches to learn from the gathered simulation information. Having developed a database which allows querying simulation information of thousands of simulations, we will on the one hand give some insights about challenges like the management of changing designs during the development phase and how to still being able to compare geometrically altered parts. On the other hand we will point out how to make use of the simulations of the entire model development branch to speed up and improve the engineer's daily work. The approach currently being developed illustrates how it is possible to automatically detect anomalies / outliers within the crash deformation behaviour, to point the engineers attention exactly to the location in space and time where the model is showing unknown or unwanted deformation patterns. While in daily work the engineer often only has time to compare single simulations with each other, this approach shows how to compare the current simulation with hundreds of predecessors at a time. As an additional benefit this approach covers analysis of the behaviour of all parts over all states, such that even areas which are not in the immediate focus of the engineer are being evaluated and highlighted if conspicuous.

Document Details

ReferenceNWC21-469-c
AuthorBorsotto. D
LanguageEnglish
TypePresentation Recording
Date 27th October 2021
OrganisationSIDACT
RegionGlobal

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