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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.
*