Fire growth modeling

Generally these codes/models are limited in ability to incorporate all of the aspects of fire while still maintaining a simple physical description of how enclosure fires develop. This requires balancing mathematical detail against physical realism. 

The zone model has the following basic limitations 1) complex enclosure geometries cannot be addres.sed, 2) forced ventilation cannot be realistically modeled using simple unit models, 7 ) burning other combustibles remote from the initiating source are not modeled, and 4) siipprcssion activities are not included. 

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Simulation of flame spread and fire growth is one of the most challenging modeling problems in fire CFD. The origin of the challenging nature of fire growth modeling is that it requires accurate simulation of several subprocesses ... 

Typically, a fire growth model is evaluated by comparing its calculations (predictions) of large-scale behavior to experimental HRR measurements, thermocouple temperatures, or pyrolysis front position. The overall predictive capabilities of fire growth models depend on the pyrolysis model, treatment of gas-phase fluid mechanics, turbulence, combustion chemistry, and convective/radiative heat transfer. Unless simulations are truly blind, some model calibration (adjusting various input parameters to improve agreement between model calculations and experimental data) is usually inherent in published results, so model calculations may not truly be predictions. 

One of the simplest large-scale geometries relevant to real world fire growth modeling is vertical upward flame spread on a free-standing wall, meaning that the wall is not part of a compartment. Compartment effects, such as accumulation of a hot ceiling layer, do not come into play. 

FIRAC is a computer code designed to estimate radioactive and chemical source-terms as.sociaied with a fire and predict fire-induced flows and thermal and material transport within facilities, especially transport through a ventilation system. It includes a fire compartment module based on the FIRIN computer code, which calculates fuel mass loss rates and energy generation rates within the fire compartment. A second fire module, FIRAC2, based on the CFAST computer code, is in the code to model fire growth and smoke transport in multicompartment stmetures. 

Tones, W. W. and R. D, Peacock, 1994 Refinement and Experimental Verification of a Model for Fire Growth and Smoke Transport, 2nd lAFSS Meeting. 

Peacock, R D., et al., 1993a, CFAST, The Consolidated Model of Fire Growth and Smoke Transport , NIST Technical Note 1299, NIST. 

Probably the best way of assessing fire hazard is by calculations via mathematical fire growth and transport models, such as HAZARD I [58], FAST [59], HARVARD [60] or OSU [61]. These models predict times to reach untenable situations. They are often combined with fire escape models and will, then, yield times to escape. 

Karlsson, B., Modeling fire growth on combustible lining materials in enclosures, Report TBVV-1009, Department of Fire Safety Engineering, Lund University, Lund, 1992. 

Techniques are available to calculate conditions under which enclosed fires are ventilation- or fuel- controlled. Computer models are available to estimate compartment fire growth and temperature effects. In particular, the zone fire model C-FAST (Jones et al., 2000) is widely used. Additional information on models is contained in Appendix C. 

Jones, W., Peacock, R., Forney, G., and Reneke, P., CFAST Consolidated Model of Fire Growth and Smoke Transport (Version 5). Technical Reference Guide., NIST Special Publication 1030, National Institute of Standards and Technology Gaithersburg, MD, 2004, p. 153. 

Hasemi, Y. and Tokunaga, T., In modeling of turbulent diffusion flames and fire plumes for the analysis of fire growth, 21st National Heat Transfer Conference, Fire Dynamics and Heat Transfer, Seattle, WA, 1983, pp. 37 15. 

Of the several approaches that have been used to calculate fuel generation rates from solid materials in CFD-based fire growth calculations, the simplest are empirical models. Instead of attempting to model the physical processes that lead to gaseous fuel production inside decomposing solids, empirical data that can be measured (transient heat release or mass loss rate) or inferred (heat of gasification) from common bench-scale fire tests such as the Cone Calorimeter are used to characterize fuel generation processes. 

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