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## CFAST: Consolidated Model of Fire Growth and Smoke Transport (Version 6). Technical Reference Guide.

CFAST: Consolidated Model of Fire Growth and Smoke Transport (Version 6). Technical Reference Guide. (2892 K)
Jones, W. W.; Peacock, R. D.; Forney, G. P.; Reneke, P. A.

NIST SP 1026; NIST Special Publication 1026; Version 6; 117 p. April 2009 (Revision).

### Keywords:

CFAST; fire growth; smoke transport; fire models; zone models; algorithms; computer programs; smoke; fire gases; temperature; compartments; gas layers; scenarios; validation; ASTM E 1355; equations; snesitivity; plumes; vents; corridors; heat transfer; ceiling jets; heat detection; sprinkler activation; flashover; verification; geometry; small scale fire tests

### Abstract:

CFAST is a two-zone fire model used to calculate the evolving distribution of smoke, fire gases and temperature throughout compartments of a constructed facility during a fire. In CFAST, each compartment is divided into two gas layers. The modeling equations used in CFAST take the mathematical form of an initial value problem for a system of ordinary differential equations (ODEs). These equations are derived using the conservation of mass, the conservation of energy (equivalently the first law of thermodynamics), the ideal gas law and relations for density and internal energy. These equations predict as functions of time quantities such as pressure, layer height and temperatures given the accumulation of mass and enthalpy in the two layers. The CFAST model then consists of a set of ODEs to compute the environment in each compartment and a collection of algorithms to compute the mass and enthalpy source terms required by the ODEs. In general, this document provides the technical documentation for CFAST along with significant information on validation of the model. It follows the ASTM E1355 guide for model assessment. The guide provides several areas of evaluation: (*) Model and scenarios definition, (*) Theoretical basis for the model, (*) Mathematical and numerical robustness, (*) Model sensitivity and (*) Model Evaluation.