Inverse modeling and characterization of an experimental testbed to advance fire scene reconstruction
MetadataShow full item record
Fire investigators examine fire scenes and collect data to form hypotheses on the origin and cause of the fire. The fire scene contains a wealth of data in the form of damage to objects in the areas affected by the fire. A computational framework with the ability to make inferences on the origin of a fire based on the data would be beneficial to the fire investigation process. Such a framework would require models of the fires, quantifiable damage metrics, and a method for making inferences on the fire origin. This work seeks to address two of the three points by using Bayesian inversion for determining the most likely origin of a fire in a compartment and constructing an algorithm for determining the heat-release rate from a burning object that can be supplied to a computational fire model. To accomplish these tasks, an experimental burn compartment was designed and a series of tests were run with controlled heat-release rates. Data collected in each experiment included temperatures, heat fluxes, and gas velocities. Modeling of the controlled heat-release rate experiments was carried out in the Consolidated Model of Fire and Smoke Transport (CFAST) and Fire Dynamics Simulator (FDS). Both the Bayesian inversion framework and heat-release rate reconstruction algorithm rely on computational fire models to determine the fire location and heat-release rate respectively. Following the modeling efforts, the Bayesian inversion framework was tested on synthetic data generated by FDS using the geometry of the experimental structure. Time-integrated total energy per unit area data were used as a placeholder for damage models of objects found in a fire scene. The heat-release rate reconstruction algorithm was used to determine the heat-release rates of the experiments using transient heat flux data collected at an array of sensors.