£6,000 FindAPhD Scholarship | APPLICATIONS CLOSING SOON! £6,000 FindAPhD Scholarship | APPLICATIONS CLOSING SOON!
  Prof Paolo Mignosa, Prof Alessia Ferrari  Applications accepted all year round  Funded PhD Project (Students Worldwide)

About the Project

The mathematical modelling of flood propagation in rural and urban areas is typically carried out using numerical codes that solve with different approaches the so-called Shallow Water Equations (SWE), i.e. a set of 1D or 2D non-linear partial differential equations. However, some of the assumptions used to derive these equations, such as the hydrostatic pressure distribution and a small channel (or valley) slope, may be in some cases far from reality: e.g. when floods generated by dam failures propagate in Alpine valleys, and near a river levee breach in lowlands. A further issue related to these catastrophic events is that buildings and infrastructures may collapse during the flood propagation. Hydrodynamic quantities obtained by combining water depth and velocity values are usually evaluated to verify the stability of these structural elements, while keeping them in the computational domain. However, the dynamic removal of these structural elements from the computational grid would lead to more realistic results.

The research aims at investigating these phenomena from both a numerical and an experimental point of view, by implementing new features in the PARFLOOD code, which is a parallel 2D model developed at the University of Parma, and carrying out laboratory benchmarks useful to validate the numerical model. Candidates are required to have basic knowledge of the theory of free surface unsteady flow and a willingness to acquire skills in the use / development of numerical models Professor  and Eng.  supervise and co-supervise the PhD candidate. Some information on the activities of the Research Group can be found at www.hylab.com

Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs