Coarse-grained modelling of fluidized beds of non-spherical particles
This project is part of a larger ERC project in which quantitative understanding of the hydrodynamics of gas-fluidised systems of non-spherical granular particles will be achieved through a combination of modelling and experimentation, with an emphasis on modelling. The models range from detailed models of particle and fluid interactions, to more coarse-grained models, which use correlations obtained from the fundamental models, for large-scale applications. The models are validated experimentally, using state-of-the-art non-invasive techniques such as particle image velocimetry, digital image analysis and magnetic particle tracking.
In the current subproject, machine learning methods such as genetic programming will be used to establish correlations between different parameters, which are involved in the fluidized gas systems. Those relations will be used to connect the various numerical methods that are used in the current project. For example from the direct numerical simulations (DNS), correlations will be established for the hydrodynamic drag and torque as a function of the orientation of the particles, and their volume fraction. Those relations will be used as input relations for the coarse-grained methods such as DPM (Discrete Particle Method. Also, during the subproject relations for the stress tensor will be obtained.