Project Name:Improving the Efficiency of Flooding Predictions via Adaptive Mesh Resolution
Other Research Participants/Partners:Casey Dietrich, North Carolina State University
Coastal communities rely on predictions of flooding caused by storms. Computational models are essential for making these predictions, but a typical prediction can require hundreds or even thousands of computational cores in a supercomputer and several hours of wall-clock time. In this project, we will improve the performance and accuracy of a widely-used, predictive model for coastal flooding. The model’s representation of the coastal environment will adapt during the storm, to better utilize the computing resources and ultimately provide a faster prediction.
Numerical methods for partial differential equations, specifically flow and transport problems in CFD; scientific computing and parallel computing; finite element analysis, discontinuous Galerkin methods; shallow water systems, hurricane storm surge modeling, rainfall-induced flooding; ground water systems, flow in porous media, geochemistry; data assimilation, parameter estimation, uncertainty and error estimation.
Presentations and Reports:
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