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Fire impact assessment in FLNG processing facilities using Computational Fluid Dynamics (CFD)

Baalisampang, T, Abbassi, R ORCID: 0000-0002-9230-6175, Garaniya, V ORCID: 0000-0002-0090-147X, Khan, F ORCID: 0000-0002-5638-4299 and Dadashzadeh, M 2017 , 'Fire impact assessment in FLNG processing facilities using Computational Fluid Dynamics (CFD)' , Fire Safety Journal, vol. 92 , pp. 42-52 , doi: 10.1016/j.firesaf.2017.05.012.

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Abstract

Increasing demand for natural gas has pushed the exploration of natural gas to remote offshore locations using a Floating LNG (FLNG) facility. In this facility, fire hazards are comparatively high and even a single fire accident may be catastrophic due to the congested and complex layout of the facility. This study proposes a novel methodology for modelling the impact of a fire event in an FLNG facility. Hazard identification and accident credibility assessment have been used to discover the three most credible fire accident scenarios. These scenarios have been simulated using Computational Fluid Dynamics (CFD) code, Fire Dynamics Simulator (FDS). The results have then been compared to identify the most severe impact of the fire on personnel and assets using thermal radiation and risk levels. It has been found that the fire event in all three scenarios has a high potential to cause damage to adjacent assets. From this comparison, it is evident that the scenario in the Mixed Refrigerant Module in the liquefaction process has the highest risk of fire to both on-board personnel and assets. The proposed methodology may be adopted further for safety measure design to mitigate or avoid the impacts of a fire event in any complex processing facility.

Item Type: Article
Authors/Creators:Baalisampang, T and Abbassi, R and Garaniya, V and Khan, F and Dadashzadeh, M
Keywords: CFD, fire modelling, accident credibility, hazard assessment, FLNG
Journal or Publication Title: Fire Safety Journal
Publisher: Elsevier B.V.
ISSN: 0379-7112
DOI / ID Number: 10.1016/j.firesaf.2017.05.012
Copyright Information:

Copyright 2017 Elsevier Ltd.

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