Publication Date

12-2024

Date of Final Oral Examination (Defense)

7-18-2024

Type of Culminating Activity

Dissertation

Degree Title

Doctor of Philosophy in Computing

Department

Mathematics

Supervisory Committee Chair

Donna Calhoun, Ph.D.

Supervisory Committee Member

Jodi Mead, Ph.D.

Supervisory Committee Member

Michal Kopera, Ph.D.

Supervisory Committee Member

David L. George, Ph.D.

Abstract

Overland flooding, a critical environmental phenomenon, poses significant challenges for computational modeling due to its complex hydrodynamics and the need for high-resolution data. This thesis presents GeoFlood, a new open-source software package for overland flooding simulations. The computational model solves shallow water equations (SWE) on a quadtree hierarchy of mapped, logically Cartesian grids managed by the parallel, adaptive library ForestClaw (Calhoun & Burstedde, 2017). The model is validated using standard benchmark tests from Neelz & Pender (2013) and against results from the GeoClaw software (George, 2011; Clawpack Development Team, 2020) for the historical Malpasset dam break problem. The benchmark test results are compared against GeoClaw and a standard software HEC-RAS (Hydraulic Engineering Center River Analysis System) results (Brunner, 2018). This comparison demonstrates GeoFlood’s ability to predict flood wave propagation accurately and efficiently on complex terrain. The results from comparisons with the Malpasset dam-break show good agreement with the GeoClaw results and are consistent with the historical records of the event. Performance tests on the Missoula and Teton dambreak problems demonstrate GeoFlood’s scalability and efficiency against GeoClaw, making it suitable for large-scale flood simulations.

To enhance simulation speed, a hybrid CPU/GPU version of GeoFlood has been implemented, leveraging the parallelism provided by the ForestClaw library and GPU acceleration through CUDA programming. The primary goal of this research aspect is to accelerate existing CPU-based patch solvers and Riemann solvers within the GeoFlood code using CUDA. The accelerated version of GeoFlood has been validated against the CPU version, demonstrating substantial improvements in both advance time (time taken to advance a solution on a patch) and wall time, as evidenced by the significant speedups observed in simulations of the Malpasset, Missoula, and Teton floods. The findings suggest that the GPU-accelerated GeoFlood can simulate overland flooding events more efficiently with more resolution but in the same time that is needed by the CPU version. This GPU-enhanced version is expected to be a valuable tool for researchers and practitioners in hydrology and hydraulic engineering, providing more precise and efficient simulations of overland flooding events, thereby improving flood disaster preparedness and forecasting capabilities.

Comments

https://orcid.org/0000-0002-0995-1051

DOI

https://doi.org/10.18122/td.2290.boisestate

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