Comparison of CFAST Predictions to USCG Real-Scale Fire Tests.

Published

January 1, 2000

Author(s)

Paul A. Reneke, Walter W. Jones, M J. Peatross, C L. Beyler, R Richards

Abstract

The zone model CFAST was used to make predictions of single room pre-flashover tire tests conducted in a steel enclosure. These results were then compared with previously published measurements obtained in fire tests. Tests included diesel pool fires, polyurethane slab fires, and wood crib fires. Half of these tests used natural ventilation (window, 1/4 door, and full door) while the remaining tests used forced ventilation (0.25 m3/s, 0.38 m3/s, and 0.61 m3/s). With the exception of heat release rates, all CFAST inputs were selected without knowledge of the experimental results. Key variables compared include the upper layer temperature, the hot layer interface location, and ceiling temperatures. Overall, predictions made by CFAST were in good agreement with the data. There was a general tendency to over predict both the hot gas layer temperature and the boundary surface temperature which may be due to under prediction of boundary heat losses. Experimental results showed that heat release rates varied with ventilation configurations by as much as a factor of 3. This observation indicates that the wide practice of using free bum heat release rate data in compartment fire predictions can result in over prediction of compartment fire conditions.

Citation

NIST Interagency/Internal Report (NISTIR) - 6446

Report Number

6446

NIST Pub Series

NIST Interagency/Internal Report (NISTIR)

Pub Type

NIST Pubs

Download Paper

https://doi.org/10.6028/NIST.IR.6446

Keywords

fire models, predictive models, zone models, fire tests, test methods, fire safety, heat release rate, smoke movement, CFAST, ventilation

Citation

Reneke, P. , Jones, W. , Peatross, M. , Beyler, C. and Richards, R. (2000), Comparison of CFAST Predictions to USCG Real-Scale Fire Tests., NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.6446 (Accessed October 9, 2024)