PhD Studentship: The derivation and integration of detailed particle characteristics to enhance par
Particle systems are ubiquitous across the environmental (riverbeds, sand dunes and avalanches, etc.), industrial (coal and biomass fluidised bed 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, and being more equipped to predict and adapt to natural events. The scale of these processes means the role computational resources for prediction and optimisation plays a crucial role.
Unfortunately, the complex interaction between particles; their co-existence in varying “dense” and “dilute” particle distributions; and the variation of particle properties within the same region of interest leads to significant challenges when predicting such processes. Despite advances being made over recent years to incorporate more detailed particle characteristics into existing particle models these are still far from reliable due to the many assumptions still being made. Many assumptions are justified, such as treating the gas and particles as fully interpenetrating phases as the scale of these applications means it is impossible to track each individual particle, despite the tremendous increase in computational capabilities. However, there are many assumptions still being made which play a significant role in the predicting the overall performance of these processes, such as assuming all particles are frictionless, uniform-sized particles and even perfectly spherical.
This project will derive and adapt numerical particle models that remove unnecessary assumptions. The models will be applied to various systems to try to capture more physical phenomena that are too difficult to capture with existing models, such as particle segregation, particle sliding, cluster formation and even particle fragmentation in highly collisional regimes.
This project will be require someone with a strong mathematically background, demonstrating a strong interest in the derivation of new methods from first principles. An understanding of the how particles interact with other particles as well as the surrounding fluids would be expected. Finally, the applicant should have coding experience, ideally with computational fluid dynamics open source packages such as OpenFOAM.
If you wish to discuss any details of the project informally, please contact Dr Lindsay-Marie Armstrong, Energy Technology research group, Email: L.Armstrong@soton.ac.uk, Tel: +44 (0) 2380 59 4760.
To apply, please use the following website: http://www.southampton.ac.uk/engineering/postgraduate/research_degrees/apply.page
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