Detailed particle modelling for clean carbon applications

Particle systems are ubiquitous across the environmental (air particles, riverbeds, sand dunes and avalanches, etc.), industrial (combustion unit reactors, and spray coating technologies, etc.) and pharmaceutical (ingredient conveying, blending, drying and capsule loading, etc.) sectors. With significant efforts being made on a global scale to tackle climate challenges there is a drive towards improving efficiency across the various industrial and power sectors; as well as being more equipped to predict and adapt to natural events. The scale of these processes means computational methods for prediction and optimisation plays a crucial role.

Despite advances being made over recent years to capture more detailed particle physics into existing particle models, they remain far from reliable due to the many assumptions still being made. These assumptions directly impact the prediction of the overall performance of these processes, such as assuming all particles are frictionless, uniform-sized particles and even perfectly spherical. Understanding and incorporating more detailed physics can support the prediction of key physical phenomena commonly experienced across many industrial and power-generation sectors, such as particle segregation, particle sliding, cluster formation and even particle fragmentation in highly collisional regimes.

This project will require someone with a strong mathematical and computational fluid dynamics background. Ideally, the applicant would have coding experience, ideally with computational fluid dynamics open source packages such as OpenFOAM.