The University of Sheffield
Department of Civil and Structural Engineering

Integrated Modelling Of Mobile Obstacles In Shallow Water Flow

Supervisor: Dr Georges Kesserwani

Joint Supervisor: Dr Shao Songdong

Conventional numerical methods have been proposed for use in flood wave modelling. Most of these methods are based on the numerical solution of the Shallow Water Equations (SWE) in two horizontal dimensions (2D), and have now become core of the latest modelling packages in the hydraulic software industry. Common to these methods, they require a mesh to solve on the SWE and therefore are mesh-based. Mesh-free methods such as SPH (Smooth Particle Hydrodynamics) have emerged more recently. Such methods are beneficial to integrate the multi-physics associated with wave-structure interaction albeit at very high computational and runtime costs. The Lattice Boltzmann Method (LBM) is a hybrid mesh-based and mesh-free approach, which thus offer the opportunity to exploit the combined benefits of both.

This work aims to construct a LBM method for the simulation of water waves and their interaction with moving obstacles. Three stages are envisaged for this project: (1) A priori formulation and implementation of the LBM method solving the SWE, (2) transfer of the practical advances featuring in "mesh-based" finite volume 2D numerical model and (3) integration of the obstacle components (for different geometries). During these stages, model versification and testing will be a constant activity; in particular relating to the handling of wave-bodies interactions. Emerging data for modelling flow with debris (i. e. dx.doi.org/10.1098/rspa.2013.0820) and moving-boding (i. e. Wu et al., WCE 2013, Vol. III) will be used for practical validation.

Suitable applicants are those with any Engineering, or Mathematics and Physics backgrounds. Experience in Computational Fluid Mechanics and in programming languages is a plus.

This project is NOT FUNDED, although Departmental/University scholarships are available for applicants who can demonstrate strong evidence of research potential.