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Flame detection by heat from the infrared spectrum: Optimization and sensitivity analysis

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posted on 2022-12-16, 09:30 authored by Hadi Bordbar, Farid Alinejad, Kevin Conley, Tapio Ala-NissilaTapio Ala-Nissila, Simo Hostikka

Accurate detection of unwanted fires at their early stage is crucial for efficient mitigation and loss prevention. Moreover, the detection strategy must avoid false alarms and the associated disruptions in workplaces. Thermal radiation-based flame detection is the fastest detection method and is commonly used in critical industrial spaces, such as air hangars and petroleum manufacturing and storage. The main challenge is distinguishing the radiation of flames from other sources, e.g., hot objects or the Sun. The principles of radiation-based flame detection have been known for a long time, but open data and worked-out feasibility studies are rare. This work takes advantage of the recent advances in experimental and numerical methods of characterizing the infrared spectra. Combining high-resolution spectra from flames and blackbody emitters with virtual low-pass filters allows us to simulate the response of a hypothetical sensor. To maximize the difference between flame and blackbody responses, we use a pattern search algorithm to find optimal filtering wavelengths for two different detection strategies based on three or four optical low-pass filters. The optimal wavelengths are reported along with the sensitivity of the detection signal to the filter non-ideality. Our results give guidelines for design of efficient and highly selective flame-radiation-based fire detection sensors.

Funding

Novel measurement and sensing technologies for thermal radiation of unwanted fires

Academy of Finland

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History

School

  • Science

Department

  • Mathematical Sciences

Published in

Fire Safety Journal

Volume

133

Publisher

Elsevier

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2022-09-12

Publication date

2022-09-16

Copyright date

2022

ISSN

0379-7112

eISSN

1873-7226

Language

  • en

Depositor

Prof Tapio Ala-Nissila. Deposit date: 15 December 2022

Article number

103673

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