Large-Scale Fire Experiments for Firefighter Tactics

Conducted large-scale experiments to characterize temperatures, heat fluxes, velocities, gas concentrations, and flow paths under various ventilation and suppression conditions.

Verification and Validation work for Fire Models

Verification and validation work with fire models, including empirical correlations, Consolidated Model for Fire Growth and Smoke Transport (CFAST), and Fire Dynamics Simulator (FDS). Developed automated verification and validation tools.

Soot and Aerosol Deposition in Fire Dynamics Simulator
Soot Deposition

In fire models, the accurate prediction of aerosol and soot concentrations in the gas phase and their deposition thicknesses in the condensed phase is important for a wide range of applications, including human egress calculations, heat transfer in compartment fires, and forensic reconstructions. During a fire, in addition to soot transport by advection and diffusion, a significant amount of soot can be deposited on surfaces due to various mechanisms. As a first approach of quantifying aerosol deposition predictions under non-reacting flow conditions, this study identified important parameters under various flow conditions and compares predicted aerosol deposition quantities to experimentally measured data. The computational tool used in this study was the computational fluid dynamics code, Fire Dynamics Simulator (FDS). Model predictions are compared to measured deposition velocities for various sizes of monodisperse fluorescent particles and various air velocities at the ceiling, wall, and floor of a ventilation duct.

Statistical (Bayesian) Inference for Fire Engineering Scenarios

Fire models are routinely used to evaluate life safety aspects of building design projects and are being used more often in fire and arson investigations as well as reconstructions of firefighter line-of-duty deaths and injuries. A fire within a compartment effectively leaves behind a record of fire activity and history (i.e., fire signatures). Fire and arson investigators can utilize these fire signatures in the determination of cause and origin during fire reconstruction exercises. Researchers conducting fire experiments can utilize this record of fire activity to better understand the underlying physics. In all of these applications, the heat release rate and location of a fire are important parameters that govern the evolution of thermal conditions within a fire compartment. These input parameters can be a large source of uncertainty in fire models, especially in scenarios in which experimental data or detailed information on fire behavior are not available.


A methodology was sought to estimate the amount of certainty (or degree of belief) in the input parameters for hypothesized scenarios. To address this issue, an inversion framework was applied to scenarios that have relevance in fire scene reconstructions. Rather than using point estimates of input parameters, a statistical inversion framework based on the Bayesian inference approach was used to calculate probability distributions of input parameters. These probability distributions contain uncertainty information about the input parameters and can be propagated through fire models to obtain uncertainty information about predicted quantities of interest. The Bayesian inference approach was applied to vari- ous fire problems using different models: empirical correlations, zone models, and computational fluid dynamics fire models. Example applications include the estimation of steady-state fire sizes in a compartment and the location of a fire.

Characterizing Heat Release Rates Using an Inverse Fire Modeling Technique


A ubiquitous source of uncertainty in fire modeling is specifying the proper heat release rate (HRR) for the fuel packages of interest. An inverse HRR calculation method was developed to determine an inverse HRR solution that satisfies measured temperature data. The methodology uses a predictor-corrector method and the Consolidated Model of Fire and Smoke Transport (CFAST) zone model to calculate hot gas layer (HGL) temperatures in single compartment configurations. The inverse method runs at super-real-time speeds while calculating an inverse HRR solution that reasonably matches the original HRR curve. Applications of the inverse method include a multiple step HRR case, complex HRR curves, experimental temperature data with a constant HRR, and a case with an experimentally measured HRR. In principle, the methodology can be applied using any reasonably accurate fire model to invert for the HRR.

Grassland Fuel Laboratory-Scale Experiments and Fire Modeling (and Wildfire-Urban Interface Fires)


This research focused on improving the understanding of the physics and fire dynamics of grassland fueled fire. Little bluestem (Schizachyrium scoparium) grass was chosen as the grassland fuel due to its prevalent coverage in the Texas area and its relevance to grassland fires in Texas. Experimental characterization included intermediate-scale experiments to characterize the mass loss rates, heat release rates (HRRs), and flame heat fluxes of burning little bluestem plants at various moisture contents. Experiments included single plant tests, multiple plant tests with no forced flow/wind, and multiple plant tests in which a forced flow was directed over the plants to simulate wind. The burning characteristics of single plants and fire spread between multiple plants was observed. The computational tool, Wildland-Urban Interface Fire Dynamics Simulator (WFDS), was also used to model the experiments using both the prescribed HRR model and the particle-based fuel element model.

Modeling Fan-Driven Flows (Positive Pressure Ventilation) for Firefighting Tactics


Airflow control has become a large part of the tactical toolbox that firefighters use to combat fires. Control of airflow requires managing the impact of environmental conditions (i.e., wind) and optimally using mechanically generated flows from fans to drive air and combustion products through predetermined vents. This research explored the ability of analytical and computational models to predict flow variables associated with the use of positive pressure ventilation. To make these predictions, it is shown that various levels of approximation and a knowledge of (the often neglected) structure leakage rates are required. This research involved experiments and simulations of airflow rates associated with fan-induced pressure differences between the environment and a structure.

Characterizing the Flammability of Storage Commodities Using an Experimentally Determined B-number

In warehouse storage applications, it is important to classify the burning behavior of commodities and rank them according to material flammability for early fire detection and suppression operations. In this study, the large-scale effects of warehouse fires are decoupled into separate processes of heat and mass transfer. As a first step, two nondimensional parameters are shown to govern the physical phenomena at the large-scale, a mass transfer number, and the soot yield of the fuel which controls the radiation observed in the large-scale. In this study, a methodology is developed to obtain a mass-transfer parameter using mass-loss (burning rate) measurements from bench-scale tests.

Two fuels are considered, corrugated cardboard and polystyrene. Corrugated cardboard provides a source of flaming combustion in a warehouse and is usually the first item to ignite and sustain flame spread. Polystyrene is typically used as the most hazardous product in large-scale fire testing. A mixed fuel sample (corrugated cardboard backed by polystyrene) was also tested to assess the feasibility of ranking mixed commodities using the bench-scale test method. The nondimensional mass transfer number was then used to model upward flame propagation on 20-30 foot stacks of Class III commodity consisting of paper cups packed in corrugated cardboard boxes on rack-storage. Good agreement was observed between the model and large-scale experiments during the initial stages of fire growth.

Characterizing Flammability of Corrugated Cardboard Using a Cone Calorimeter
Link to poster
2008 – 2009

In warehouse storage applications, it is important to classify the burning of cardboard because it provides a source of flaming combustion and is usually the first item to ignite and sustain flame spread. This study develops a methodology to obtain a non-dimensional mass transfer number (or Spalding’s B-number) by using the mass loss measurements from a cone calorimeter. The small-scale experimental measurements are used to model upward flame propagation on a 20-30 foot high rack-storage warehouse commodity packed in corrugated cardboard boxes. Good agreement is observed between the simple model and large scale experiments during the initial stages of fire growth.

Small-Scale Compartment Commodity Testing
2008 – 2009

The purpose of this work is to reduce the scale of commodity tests (group A polystyrene cups and class III paper cups) in order to generalize the heated conditions of the individual cells or compartments as they burn in order to create a compartmental model that is independent of the type of fuel inside. Ultimately, the benefit of this small-scale approach to the commodity tests is that the data and simplified model can be applied to many different problems such as fire suppression or fire modeling.

Fire Model Validation: Burning Rate of a Small Pool of Ethanol in a Glass Pan

This test was performed in the Fire Dynamics course (Professor: Dr. Alberto Gomez-Rivas) for the Safety and Fire program at the University of Houston-Downtown (UHD). This writeup describes the experimental setup of the test as well as the fire model inputs used in Fire Dynamics Simulator (FDS). With this data, we can obtain a mass loss rate throughout the experiment. Using the data and FDS, we can compare the observed vs. predicted mean flame height, mass over time, mass loss rate, and time to consume all fuel.

Fire Spread and Structural Stability in an Open Arena Fire Model

Fire Dynamics Simulator was used to evaluate many aspects of an arena fire model with exposed internal beam supports, including the structural stability, time to evacuation, influence of sprinklers on the model, and the effect of the heavy fuel load on the fire growth. The results show us that, without fire sprinklers, the building is in danger of collapsing within minutes of the growth stage of the fire and will present a fatal danger of heat and smoke to all occupants within an ever shorter time. The model that included sprinklers had the nearest single sprinkler head activate and extinguish the model in roughly 1.5 minutes with little to no danger to the occupants or structural elements.

Verification and Validation of Fire Models: A Model of Thermally-Induced Cable Failure (THIEF)

The primary objective of CAROLFIRE is to characterize the different modes of electrical failure within bundles of cable used in nuclear power plants for power, control, instrumentation, and systems monitoring. A secondary objective is to create a simple thermal model of a single cable that predicts electrical failure when a given interior region of a cable reaches a certain temperature.

Evaluating Smoke Detector Spacing Guidelines Using Fire Modeling and Simulation

Fire modeling is used to evaluate the effectiveness of different smoke detector spacings. Currently in the U.S., the most widely used and accepted spacing between smoke detectors is 30 feet, as recommended by many leading fire alarm codes. From the results of this project, the 30 foot spacing appeared as a good compromise of economic detector coverage with the priority of life safety and an early warning to immediate fire conditions.