Neural network supported surrogate models for particle-laden flow

Speaker: Fateme Darlik (Faculty of Science Technology and Medicine, University of Luxembourg)
Title: Neural network supported surrogate models for particle-laden flow
Time: Wednesday, 2021.05.12, 10:00 a.m. (CET)
Place: fully virtual (contact Dr. Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion

Abstract: This project focuses on coupling a data-driven model in conjunction with CFD (Computational Fluid Dynamics) to predict the behavior of biomass particles in a fixed bed. This problem (particle-fluid problem) can be solved by two-way coupling CFD and XDEM. Since this methodology is often computationally expensive, two solutions are proposed. Firstly, the neural network (using TensorFlow) is used as a surrogate model to replace XDEM. Afterward, this surrogate model is coupled with the CFD method to solve the particle-fluid problem employing preCICE (Precise Code Interaction Coupling Environment). An alternative approach assumes the behavior of dense particles in the biomass bed similar to that of fluid (of unknown material parameters). The neural network is used to identify the properties of the fluid. Having the properties of the fluid, the CFD method is used solely to solve the mentioned biomass problem.