Assessing Fire Smoke to Predict Backdraft and Smoke Explosion Potential

Published

June 3, 2020

Author(s)

Ryan Falkenstein-Smith, Thomas Cleary

Abstract

The Fire Research Division at the National Institute of Standards and Technology is investigating the ability to forecast backdraft or smoke explosions during a fire event using a phi meter. Compared to other gas sensors, a phi meter can measure the global equivalence ratio under a broad spectrum of fuel-rich and fuel-lean conditions. In this work, predetermined gas mixtures are fed into the phi meter wherein the global equivalence ratio is measured. The measured and expected global equivalence ratios for all gas mixtures are observed to be in substantial agreement. Concentration measurements are verified using the carbon to hydrogen ratio. Real-time temperature measurements are made at the inlet and outlet of the phi meter. The estimated enthalpy of the phi meter is observed to increase as the gas mixture approaches stoichiometric conditions. The work demonstrates the potential to relate the estimated enthalpy in the phi meter with its oxygen consumption. The correlation can eventually be expanded to a variety of different fuels and conditions. Once established, the repository of data sets can be correlated to the bounding conditions for actual backdraft and smoke explosions of various strengths.

Proceedings Title

17th International Conference on Automatic Fire Detection (AUBE 20) & Suppression, Detection and
Signaling Research and Applications Conference (SUPDET 2020)

Conference Dates

September 15-17, 2020

Conference Location

Mulheim an der Ruhr

Pub Type

Conferences

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Keywords

Backdraft, Smoke Explosions, Phi Meter, Flammability Limit, Gas Sampling

Citation

Falkenstein-Smith, R. and Cleary, T. (2020), Assessing Fire Smoke to Predict Backdraft and Smoke Explosion Potential, 17th International Conference on Automatic Fire Detection (AUBE 20) & Suppression, Detection and Signaling Research and Applications Conference (SUPDET 2020), Mulheim an der Ruhr, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930245 (Accessed December 2, 2024)