Particle Clustering in an Inflow/Outflow Configuration

The project Particle Clustering in an Inflow/Outflow Configuration (CLIO) investigates the behavior of solid particles settling through a fluid such as air or water. Understanding this phenomenon is essential in many natural and industrial processes, from volcanic eruptions and avalanches to chemical reactors and fluidized beds. Scientists often model these flows by assuming that particles follow the streamlines of the flow and are almost evenly distributed, which is a sometimes useful, but strong simplification. However, in reality, particles do not strictly follow the streamlines of the flow and, under some circumstances, they also form clusters where particles are found to be very close to each other. This phenomenon, known as clustering, leads to strong heterogeneities and complex interactions that make predicting its behavior an open challenge in Fluid Mechanics. In this project, we use an efficient method in which we are able to follow for very long time intervals the gravity-driven settling of ensembles of thousands of particles. This represents a more realistic configuration than those used in previous studies, allowing us to observe how particles naturally group or disperse as they settle. Using highly detailed computer simulations that resolve the flow around every particle and the interaction between them, we will explore how clusters form, evolve, and modify the behavior of the surrounding flow. Are clusters stable? Do they break apart - and how? These are typical questions we aim to answer in this project. The simulations are performed at Austrian supercomputing centres such as the Austrian Scientific Computing (ASC), using thousands of processors running in parallel for weeks or even months. These large-scale computations generate unique data that will help improve our understanding of particle-laden flows and support the development of new physical models. The results will be disseminated within the scientific community for further analysis and model validation. The project combines expertise in numerical simulation, high-performance computing, and the physics of particle-laden flows. As a summary, CLIO contributes to more accurate predictions of natural and industrial systems like rivers, clouds and chemical reactors.