University of Texas at Austin

Project Name:

Improving the Efficiency of Flooding Predictions via Adaptive Mesh Resolution

Other Research Participants/Partners:

Casey Dietrich, North Carolina State University

Project Description:

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.

Project Abstract

Research Interests:

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.


Contact Info:

Phone: (512 475-8627