Year of Award

2026

Document Type

Thesis

Degree Type

Master of Science (MS)

Degree Name

Computer Science

Department or School/College

College of Science

Committee Chair

Jesse Johnson

Commitee Members

Jacob Downs, Carl Seielstad

Keywords

wildfire, simulation, GPU, fire spread, visualization, geospatial, game engine

Subject Categories

Fluid Dynamics | Other Environmental Sciences | Other Forestry and Forest Sciences | Software Engineering | Systems Architecture

Abstract

Wildfires are growing in frequency, size, and severity, placing escalating demands on firefighting resources and putting lives, property, and ecosystems at risk. Accurate prediction of fire behavior is critical to suppression planning, evacuation decisions, and firefighter training, yet the operational models that fire managers rely upon face a persistent tradeoff. High-fidelity physics-based models capture fire–atmosphere interactions faithfully but are computationally expensive, often running far slower than real time on CPU hardware, while faster empirical models sacrifice the dynamic coupling between a fire and the wind field it generates. Furthermore, most existing tools produce abstract, two-dimensional outputs that offer limited visual intuition for the complex, three-dimensional behavior of a spreading fire. This shortcoming constrains their value for training and public communication.

This thesis presents a real-time, interactive wildfire simulation framework that addresses both sides of this tradeoff by running coupled fire physics live inside a game engine. Built on Unreal Engine 5 and executing entirely on the GPU, the framework couples a modified two-dimensional Rothermel surface-fire solver to a three-dimensional CFD wind field at every simulation step: combustion forces the atmospheric volume, and the resulting wind field feeds back to direct subsequent spread. Keeping all simulation state on device eliminates the CPU-GPU transfer bottleneck and sustains interactive frame rates while preserving the fire-atmosphere coupling that empirical models discard. Modeled rates of spread and shape are compared against established and simulated fire-behavior references, and the framework renders fire and smoke volumetrically within the same application, removing the export-then render step of prior visualization tools. The result is a responsive platform for visualizing fire behavior and its environmental feedback, with applications in fire planning, training, and exploration.

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