Modelling flow in hydrocyclones using physics-informed neural networks
Hydrocyclones are important pieces of equipment used within the mineral processing industry, where they are used for sorting ore particles suspended in water based on their size. The turbulent flow within a hydrocyclone is today usually modelled using demanding computational fluid dynamics (CFD) simulations. In this thesis, we explore the use of physics-informed neural networks (PINNs) as an alter
