Fateme DARLIK will defend her PhD thesis Physically-informed Neural Networks to Represent Motion of Granular Material on Thursday, 29 June 2023, 13h00-16h00, Campus Belval in room MNO.1010.
The growing importance of renewable energy sources, particularly biomass, in mitigating climate change has led to increased research and development in this field. Biomass combustion chambers play a crucial role in converting biomass into heat energy efficiently and cleanly. However, the combustion process is influenced by the characteristics of biomass particles, such as their composition, size distribution, and moisture content, which can vary significantly. In this thesis, we explore the application of physics-informed neural networks (PINNs) for predicting particle motion in biomass systems, aiming to improve combustion efficiency and reduce emissions.