Abstract
We model wind velocity fields for applications in wildfire modeling. We improve the accuracy of the mass-conserving WindNinja solver without compromising efficient temporal complexity. We consider two approaches: the parameterization method and the neural network method. In the parameterization method, we analyze eddies in the wake of obstacles by parameterizing cavity zones as inspired by the research by Kaplan & Dinar in 1996. Kaplan & Dinar parameterize cavity zones around rectangular buildings, and we approximate a continuous landscape with rectangular buildings in order to extend this parameterization to complex terrains. In the neural network method, we train a U-net CNN on a dataset consisting of outputs of the mass-conserving solver, outputs of the CFD solver, and elevation grids. We achieve high accuracy in approximating the output of the CFD solver with the neural network approach while retaining efficient temporal complexity.
Degree
MS
College and Department
Mathematics
Rights
https://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Housley, Laura Jane, "Improving Efficiency of Wind Flow Modeling for Wildfire Applications Using Analytical Parameterization and Neural Networks" (2025). Theses and Dissertations. 10820.
https://scholarsarchive.byu.edu/etd/10820
Date Submitted
2025-04-22
Document Type
Thesis
Handle
http://hdl.lib.byu.edu/1877/etd13624
Keywords
windflow, parameterization, cavity zone, neural network, U-net, wildfire, machine learning, WindNinja
Language
english