Combining Simulation and Machine Learning for the Optimization of Powder Handling Processes with Altair Portfolio of Tools

Altair EDEM is a discrete element method solver that enables users to accurately simulate particulate processes. The rapid virtual optimization of industrial processes requires the development of computationally efficient methodologies that go beyond a purely simulation driven approach to include machine learning based reduced-order modelling. This is particularly true in the context of industrial bulk solids handling processes, where the computational expense of high-fidelity simulations is significant. An efficient virtual optimization methodology for industrial bulk handling processes, which combines high-fidelity numerical modelling in EDEM with statistical and machine learning methods in Altair® HyperStudy®, Activate® and romAI to significantly reduce the computational expense of optimization relative to a purely simulation driven approach is beneficial.