NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.
Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.
An official website of the United States government
Here’s how you know
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)
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 October 7, 2025)