Improving the suitability of Immersed Boundary methods to model granular material

Embankment dams consisting of broadly graded materials have been found to be susceptible to internal erosion. The need to minimise both the cost and risk requires an accurate characterisation of a range of materials, in terms of their susceptibility. Despite significant theoretical and experimental developments, there is a lack of consensus as to how to determine the susceptibility of a material to internal erosion, and what force conditions are required to initiate erosion in susceptible materials. A significant weakness in current geotechnical modelling is the limitation to the use of a large-scale averaged representation of the flow. The immersed-boundary method alternative is a small-scale resolving method capable of elucidating the flow at arbitrarily small scales, rectifying this limitation. In this research the Immersed-Boundary Method (IBM) is used to accurately model the interaction of the particle surface and the fluid flow. The method is adapted and applied to modelling internal erosion, with the intention of understanding the micro-scale mechanisms involved. In particular, the fluid-particle coupling in the IBM model is adapted to be suitable to the case of densely packed particles, characteristic of embankment structures. In this thesis significant improvements are made to the immersed-boundary method’s ability to model fluid particle flows. Improvements to the momentum exchange between the particles surface both when the particle is removed from others, and when two particles are within each others range of influence, are made. Additionally the dependence on mesh resolution of the flow field in proximity of the particle’s surface is reduced, allowing more tractable simulation of the large scale problems characteristic of geotechnical problems. The removal of sporadic pressure fluctuations improves the accuracy of predictions. The resulting tool will provide insight into the science under-pinning internal erosion, guiding best practice with regards to predicting and preventing the onset and propagation of internal erosion.Open Acces