Developing an enhanced thermal radiation model through a Semi-A priori approach

The accurate prediction of thermal radiation is crucial for effective fire safety assessment and the development of proactive prevention strategies. This article presents an innovative approach to thermal radiation modelling through the integration of a semi-a priori methodology. The proposed model...

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Main Authors: Chong, Michael Vui San, Mohd Tohir, Mohd Zahirasri, Saadon, Syamimi, Abu Talib, Abd Rahim
Format: Article
Published: Elsevier Masson 2024
Online Access:http://psasir.upm.edu.my/id/eprint/105855/
https://www.sciencedirect.com/science/article/pii/S1290072923006452
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spelling my.upm.eprints.1058552024-03-21T09:21:13Z http://psasir.upm.edu.my/id/eprint/105855/ Developing an enhanced thermal radiation model through a Semi-A priori approach Chong, Michael Vui San Mohd Tohir, Mohd Zahirasri Saadon, Syamimi Abu Talib, Abd Rahim The accurate prediction of thermal radiation is crucial for effective fire safety assessment and the development of proactive prevention strategies. This article presents an innovative approach to thermal radiation modelling through the integration of a semi-a priori methodology. The proposed model combines the strengths of a priori modelling and data-driven approaches, utilising the well-established single point source model as a foundation. The proposed model undergoes refinement and development using experimental data involving propane gas fires, ethanol pool fires, and isopropyl alcohol pool fires. These datasets serve as fitting data, enabling the model to improve its accuracy and performance. The refined model's performance is further assessed through the validation process using experimental data and simulation data. Experimental data from different fire scenarios, i.e., acetone and methanol pool fires, are utilised for the validation. Additionally, the estimated heat flux values generated by the proposed model and the single point source model are also compared with simulation results obtained from Fire Dynamics Simulator (FDS), specifically for heptane and methane pool fires. The findings of this research highlighted the enhanced accuracy and reliability of the proposed model in predicting thermal radiation behaviour. This research offers insights into thermal radiation modelling, providing valuable information for fire safety practitioners and researchers in enhancing fire risk assessment and mitigation strategies. By combining physical principles with empirical data, the proposed approach offers an alternative method for thermal radiation prediction in diverse fire scenarios. Elsevier Masson 2024-03 Article PeerReviewed Chong, Michael Vui San and Mohd Tohir, Mohd Zahirasri and Saadon, Syamimi and Abu Talib, Abd Rahim (2024) Developing an enhanced thermal radiation model through a Semi-A priori approach. International Journal of Thermal Sciences, 197. art. no. 108784. pp. 1-13. ISSN 1290-0729 https://www.sciencedirect.com/science/article/pii/S1290072923006452 10.1016/j.ijthermalsci.2023.108784
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description The accurate prediction of thermal radiation is crucial for effective fire safety assessment and the development of proactive prevention strategies. This article presents an innovative approach to thermal radiation modelling through the integration of a semi-a priori methodology. The proposed model combines the strengths of a priori modelling and data-driven approaches, utilising the well-established single point source model as a foundation. The proposed model undergoes refinement and development using experimental data involving propane gas fires, ethanol pool fires, and isopropyl alcohol pool fires. These datasets serve as fitting data, enabling the model to improve its accuracy and performance. The refined model's performance is further assessed through the validation process using experimental data and simulation data. Experimental data from different fire scenarios, i.e., acetone and methanol pool fires, are utilised for the validation. Additionally, the estimated heat flux values generated by the proposed model and the single point source model are also compared with simulation results obtained from Fire Dynamics Simulator (FDS), specifically for heptane and methane pool fires. The findings of this research highlighted the enhanced accuracy and reliability of the proposed model in predicting thermal radiation behaviour. This research offers insights into thermal radiation modelling, providing valuable information for fire safety practitioners and researchers in enhancing fire risk assessment and mitigation strategies. By combining physical principles with empirical data, the proposed approach offers an alternative method for thermal radiation prediction in diverse fire scenarios.
format Article
author Chong, Michael Vui San
Mohd Tohir, Mohd Zahirasri
Saadon, Syamimi
Abu Talib, Abd Rahim
spellingShingle Chong, Michael Vui San
Mohd Tohir, Mohd Zahirasri
Saadon, Syamimi
Abu Talib, Abd Rahim
Developing an enhanced thermal radiation model through a Semi-A priori approach
author_facet Chong, Michael Vui San
Mohd Tohir, Mohd Zahirasri
Saadon, Syamimi
Abu Talib, Abd Rahim
author_sort Chong, Michael Vui San
title Developing an enhanced thermal radiation model through a Semi-A priori approach
title_short Developing an enhanced thermal radiation model through a Semi-A priori approach
title_full Developing an enhanced thermal radiation model through a Semi-A priori approach
title_fullStr Developing an enhanced thermal radiation model through a Semi-A priori approach
title_full_unstemmed Developing an enhanced thermal radiation model through a Semi-A priori approach
title_sort developing an enhanced thermal radiation model through a semi-a priori approach
publisher Elsevier Masson
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/105855/
https://www.sciencedirect.com/science/article/pii/S1290072923006452
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score 13.160551