A non-Gaussian Approach to Heavy Particle Simulation in Turbulent Flow

Abstract

The behaviour of heavy particles suspended in turbulence is of vital importance in many branches of science. It gives insight into droplet formation in clouds, plankton distribution in the oceans and pollen carried by the wind, among other things. Turbulent systems are highly dependent on the system parameters, making experimental observation and classical simulations difficult. However, statistical models of these systems can often give insight into the dynamics of suspended particles. In this thesis such a statistical model is constructed, and the dynamics of inertial particles in such a flow are analyzed. Our main focus is on how non-Gaussian flows affect preferential sampling, and we will also look into void formation and fractal clustering.