PhD defence: Mechanism-Based Upscaling of Particle Clogging in Porous Media: An Integrated Experimental and Numerical Approach

This thesis explores how particles clog porous media — materials filled with tiny, interconnected pores, like those found in filters, soils, or paper. Clogging in these materials reduces how easily fluids can pass through, affecting technologies ranging from water filtration and groundwater production to inkjet printing.

To study this phenomenon, a custom-built microfluidic setup was developed, allowing real-time observation of how particles move, attach to pore walls, and gradually slow down fluid flow. This setup combines Fluorescence Recovery After Photobleaching (FRAP) — a technique for tracking fluid movement — with pressure measurements to monitor clogging as it occurs. The experiments revealed that small particles tend to travel deeper and cause more clogging, while larger particles form surface layers that trap the smaller ones. In some cases, this leads to the formation of narrow bypass channels known as wormholes.

To better simulate and understand these processes, a Pore Network Model (PNM) was enhanced using rules derived from the experimental findings. This model simplifies the porous structure into a network of pores and connections, and predicts how particle buildup affects flow, including reduced permeability and changing flow paths. Together, the experimental insights and improved simulations offer a clearer, more accurate understanding of how clogging develops in porous media — supporting the design of better systems to manage or prevent it.