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Characterizing Smoke Dispersion Using Salt-Water Modeling

NCJ Number
Date Published
August 2013
76 pages
Andre W. Marshall
This study validated the use of salt-water analog modeling as an effective diagnostic, predictive, and scaling tool for determining fire dispersion in complex compartments, using the Particle Image Velocimetry (PIV) and Planar Laser Induced Fluorescence (PLIF) techniques.
The primary objective of this study was to examine the use of salt-water modeling as a predictive tool for determining the response time of ionization-type smoke detectors. Salt-water modeling is an example of a physical model to study fire-induced flows. In this modeling technique, salt-water is introduced into fresh water to create a buoyancy-induced flow similar to that established in a fire. The current project evaluated the strengths and weaknesses of this technique and the practicality of using this model to characterize the response of ionization-type smoke detectors in a typical fire scenario. PLIF and PIV allow non-intrusive measurements to be taken within the entire spatial domain of a planar section of the flow. This study's implementation of the PLIF technique measures the salt-water concentration by tracking a fluorescent dye tracer that is diluted at the same rate as the salt water. These planar measurements are superior to point measurements, because they reveal the instantaneous spatial relationships that are important for understanding complex turbulent flows. PIV provides velocity information within an illuminated planar section of the flow for use along with the PLIF salt-water concentration measurements. It is apparent that the smoke detector response must correlate in some manner with the dispersion characteristics of the fire. The quantitative results of the PIV and PLIF technique allowed for the time evolution of dispersion parameters from the salt-water model and the full-scale fire to be compared with a detailed examination of the smoke detector activation event. 30 figures, 4 tables, and 41 references.

Date Created: September 18, 2017