Abstract
The obscuration thresholds for various smoke detectors and combustibles, required as an input parameter in fire simulation, were measured to predict the accurate activation time of detectors. One ionization detector and nine photoelectric detectors were selected. A fire detector evaluator, which can uniformly control the velocity and smoke concentration, was utilized. Filter paper, liquid fuels, and polymer pellets were employed as smoke-generation combustibles. The nominal obscuration thresholds of the considered detectors were 15 %/m, but the ionization detectors activated at approximately 40 %/m and 16 %/m, respectively, on applying filter paper and kerosene. In contrast, the reverse obscuration thresholds were found quantitatively according to the combustibles in the photoelectric detector. This phenomenon was caused by differences in the color of the smoke particles according to the combustibles, which is explained by single-scattering albedo (ratio of light scattering to light extinction). The obscuration thresholds for liquid fuels (kerosene, heptane and toluene) as well as fire types of polymer plastic pellets were also measured for several photoelectric detectors. A database of obscuration thresholds was thereby established according to the detector and combustible types, and it is expected to provide useful information for predicting more accurate detector activation time and required safe egress time (REST).
Highlights
To reduce the risk of fire due to the manhattanization, increased sizes and complexity of buildings, the number of countries introducing performance-based fire safety design (PBD) methods has been increasing [1]
Obscuration Threshold of Ionization and Photoelectric Detectors according to Smoke Particle Colors
Smoke detectors can generally be classified into ionization-type and photoelectric-type detectors based on the detection method
Summary
To reduce the risk of fire due to the manhattanization, increased sizes and complexity of buildings, the number of countries introducing performance-based fire safety design (PBD) methods has been increasing [1]. The PBD approach generally assesses the fire risk based on a comparison between the available safe egress time (ASET) and the required safe egress time (RSET). This is based on a timeline analysis owing to the limitations of a complex review of the various factors that may affect the assessment. To improve the reliability of fire risk assessment through PBD, the ASET and RSET must first be accurately calculated
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