Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

A binary logistic regression model to evaluate backdraft phenomenon

Published

Author(s)

Ryan Falkenstein-Smith, Thomas Cleary

Abstract

A technique to predict backdraft phenomenon using a binary logistic regression model is presented. The model is established from time-averaged temperature, global and local equivalence ratios, and oxygen concentration measurements obtained in a series of backdraft experiments conducted at the National Fire Research Laboratory at the National Institute of Standards and Technology. The experiments utilized methane and propane fires of different sizes in a reduced-scale enclosure to create conditions conducive to a backdraft phenomenon. Time-averaged measurements estimated immediately before an anticipated backdraft were observed to vary with the duration of the total fuel flow time into the compartment. The established model's accuracy was found to improve with the inclusion of all time-averaged measurements as opposed to fewer components.
Proceedings Title
Suppression, Detection and Signaling Research and Applications Conference
(SUPDET 2022)
Conference Dates
September 12-16, 2022
Conference Location
Atlanta, GA, US

Keywords

Backdraft, binary logistic regression model, time-averaged measurements, Reduced-scale enclosure

Citation

Falkenstein-Smith, R. and Cleary, T. (2022), A binary logistic regression model to evaluate backdraft phenomenon, Suppression, Detection and Signaling Research and Applications Conference (SUPDET 2022) , Atlanta, GA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935430 (Accessed May 21, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created September 30, 2022, Updated November 29, 2022