The first torsional mode frequency value is a key value for concept cars. Having a trained algorithm, which provides answers based on a Finite Element Body-in-White model, without running a solver analysis, accelerates the digital model validation process.
Front crash is one of the most common finite element analyses during vehicle development. The Machine Learning functionality implemented in KOMVOS can be trained to predict the behavior of theoretical designs in a front crash, without running the full FE analysis. This enables the performance of multiple “what-if” studies without requiring the otherwise additional design and solver run time.
Torsional stiffness and torsional angle are among the most important key values in a vehicle’s Body in White (BiW) development. Using a trained Machine Learning model the identification of these values can be predicted in a fraction of the time needed for re-designing and running again the analysis.
In this Rev-Sim Guest Post, OnScale CTO, Dr. David Freed shares his views on the coming decade. Freed believes that engineering simulation will be hugely impacted by, and become inextricably entwined with, machine learning / artificial intelligence (ML/AI). Do you agree? Read all about it in this Rev-Sim Guest Post!