Efficient Material Model Calibration using Altair EDEM, Altair romAI, and Altair HyperStudy

Ambrish Singh, Solutions Engineer at Altair presents at the 2024 ATCx DEM.

Material model calibration in DEM studies often involves an iterative approach of running simulations at set input parameters and comparing results to an experimental bulk response. This introduces high computational expense and time. To minimize this effort, a trained AI model can be implemented that predicts results that a physics-based simulation would have given under identical inputs. This lowers the computation cost, and one can arrive at calibrated model parameters relatively quickly. In this talk, we take the FT4 Rheometer as an example and construct a romAI model using training data from EDEM. This trained model is later used to arrive at the calibrated parameters of a DEM simulation.