Understanding the dynamics of particle transport and retention in porous media is critical to numerous energy and environmental engineering processes. Such flow scenarios in the real world are affected by the heterogeneity of porous media and particles, geometric complexity of the flow and multiphysics mechanisms. However, traditional mathematical models for suspension flow and fines migration apply averaged large scale mass balance and kinetic equations, which do not reflect the particle and pore size distributions and microscale conditions of mechanical equilibrium.
This project aims to develop a novel multiscale method based on stochastic population balance equation and microscale network model. Macroscale transport properties are obtained from modelling of both the pore-scale and particle-scale physics via high-resolution simulations using a mesh-free Lagrangian fluid flow solver. This solver has been in development in-house and used advanced high performance capabilities such as heterogeneous parallel algorithms. High-resolution multiphysics simulations using this solver, the development of inter-particle force models, characterisation of complex geometry and utilisation of advanced data science tools are involved to achieve this goal.
The high resolution simulations will be first validated through idealised porous media with monodisperse particles and later three dimensional scans of real porous media with known particle distribution will be used. This new multiscale method has the potential to become a powerful tool with broad applications in engineering and natural processes, such as enhanced hydrocarbon recovery, optimised subsurface fluid injection accompanied with fines migration (e.g. underground carbon dioxide and hydrogen storage) and microbial propagation in aquifers.
Basic programming skills, solid foundation in fluid mechanics.
Stochastic methods, high performance computing.
Bachelors degree, Masters degree.
Multiscale modelling data science porous media complex flows.