A transport model for prediction of wildfire behavior
Wildfires are a threat to human life and property, yet they are an unavoidable part of nature. In the past people have tried to predict wildfire behavior through the use of point functional models but have been unsuccessful at adequately predicting the gross behavior of the broad spectrum of fires that occur in nature. The majority of previous models do not have self-determining propagation rates. The author uses a transport approach to represent this complicated problem and produce a model that utilizes a self-determining propagation rate. The transport approach allows one to represent a large number of environments including transition regions such as those with nonhomogeneous vegetation and terrain. Some of the most difficult features to treat are the imperfectly known boundary conditions and the fine scale structure that is unresolvable, such as the specific location of the fuel or the precise incoming winds. The author accounts for the microscopic details of a fire with macroscopic resolution by dividing quantities into mean and fluctuating parts similar to what is done in traditional turbulence modelling. The author develops a complicated model that includes the transport of multiple gas species, such as oxygen and volatile hydrocarbons, and tracks the depletion of various fuels and other stationary solids and liquids. From this model the author also forms a simplified local burning model with which he performs a number of simulations for the purpose of demonstrating the properties of a self-determining transport-based wildfire model.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE Assistant Secretary for Human Resources and Administration, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 505313
- Report Number(s):
- LA-13334-T; ON: DE97007825; TRN: AHC29716%%65
- Resource Relation:
- Other Information: DN: Thesis presented to the Dept. of Mechanical Engineering, New Mexico State Univ., Las Cruces, NM (US); TH: Thesis (Ph.D.); PBD: Jul 1997
- Country of Publication:
- United States
- Language:
- English
Similar Records
FIRETEC: A transport description of wildfire behavior
Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior