Modelling the impact of particle size and connectivity on the performance of solid oxide fuel cell electrodes

Most of the subsurface materials, such as reservoir rocks, soils and other porous media are highly heterogeneous and many of them contain a complex pore structure with a wide range of length scales. Meanwhile, quantifying heterogeneity of subsurface is an essential component of reservoir characterization process and environmental risk assessment, which is the key to run a three-dimensional geological and fluid flow simulation models. Hence, the creation of a multiscale model to quantitatively characterize such heterogeneous porous media and predict representative multi-phase flow properties is an important step to reduce the uncertainty from sample size scale to field scale. Multiscale pore scale models of the porous media are essential for such research. Apart from the basic understanding of the physics of flow processes, this has also enormous practical importance in terms of improving uncertain estimates of recovery efficiency on larger scales.

One of the main objectives of pore scale physics research is to predict macroscopic fluid flow properties from the underlying microstructure. For heterogeneous porous media materials (e.g., carbonates, soils, inhomogeneous sandstones, unconventional rocks etc.), a comprehensive description of the real pore structure may involve many decades of length-scales (e.g., from sub-micron to cm), which cannot be captured by a single-resolution microCT image. The proposed research is aimed at developing a comprehensive model to represent the real pore structure which may involve many decades of length-scales. The combination of in-house multiscale image modelling with multiscale pore network has the potential to provide an efficient way to accommodate such multi scale heterogeneity of the subsurface system.

The successful candidate should have, or expect to have, an Honours Degree at 2.1 or above (or equivalent) in Engineering, Physics, Material Science or Computing Science.

Essential Background: Basic programing and computing skill.

Knowledge of: Porous media, programing and mathematics, image processing or material mechanics would be advantageous.