## CGU Theses & Dissertations

2012

#### Degree Type

Open Access Dissertation

#### Degree Name

Engineering and Industrial Applied Mathematics Joint PhD with California State University Long Beach, PhD

#### Program

School of Mathematical Sciences

Ellis Cumberbatch

Burkhard Englert

Eun Heui Kim

#### Abstract

Wildfire hazard and its destructive consequences have become a growing issue around the world especially in the context of global warming. An effective and efficient fire simulation model will make it possible to predict the fire spread and assist firefighters in the process of controlling the damage and containing the fire area. Simulating wildfire spread remains challenging due to the complexity of fire behaviors. The raster-based method and the vector-based method are two major approaches that allow one to perform computerized fire spread simulation. In this thesis, we present a scheme we have developed that utilizes a level set method to build a fire spread simulation model. The scheme applies the strengths and overcomes some of the shortcomings of the two major types of simulation method. We store fire data and local rules at cells. Instead of calculating which are the next ignition points cell by cell, we apply Huygens' principle and elliptical spread assumption to calculate the direction and distance of the expanding fire by the level set method. The advantage to storing data at cells is that it makes our simulation model more suitable for heterogeneous fuel and complex topographic environment. Using a level set method for our simulation model makes it possible to overcome the crossover problem. Another strength of the level set method is its continuous data processing. Applying the level set method in the simulation models, we need fewer vector points than raster cells to produce a more realistic fire shape. We demonstrate this fire simulation model through two implementations using narrow band level set method and fast marching method. The simulated results are compared to the real fire image data generated from Troy and Colina fires. The simulation data are then studied and compared. The ultimate goal is to apply this simulation model to the broader picture to better predict different types of fires such as crown fire, spotting fires, etc.

Fourth committee member: Chung-min Lee

#### DOI

10.5642/cguetd/72

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