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Predicting liquefied natural gas (LNG) rollovers using computational fluid dynamics
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Hubert, Antoine, Dembele, Siaka, Denissenko, Petr and Wen, Jennifer X. (2019) Predicting liquefied natural gas (LNG) rollovers using computational fluid dynamics. Journal of Loss Prevention in the Process Industries, 62 . 103922. doi:10.1016/j.jlp.2019.103922 ISSN 0950-4230.
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Official URL: http://dx.doi.org/10.1016/j.jlp.2019.103922
Abstract
At least 24 Liquefied Natural Gas (LNG) rollover incidents have been reported since 1960. During rollover, because of the heat ingress through the tank walls, a stratified LNG may be suddenly homogenised while releasing massive amounts of vapour. The latter can result in an overpressure in the tank and significant amounts of potentially explosive LNG vapour being vented out. Both represent considerable hazards.
The current study is aimed at developing and validating rolloverFoam, a dedicated solver for simulating rollover phenomena within the frame of the open-source CFD toolbox OpenFOAM. The code is based on the Navier-Stokes equations and integrates the Boussinesq approximation in the momentum equation and the modelling of the transport of the different hydrocarbons constituting LNG. The traditional Reynolds-Averaged Navier-Stokes approach is used for computational efficiency in large-scale applications. For turbulence modelling, the model with additional terms to incorporate buoyancy effects is used.
The code has firstly been successfully validated by comparing its predictions to data obtained during a medium-scale LNG rollover experiment. The newly developed solver has also been applied to the La Spezia incident. The results have provided interesting insights into the phenomenon.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Journal or Publication Title: | Journal of Loss Prevention in the Process Industries | ||||||||
Publisher: | Elsevier Ltd | ||||||||
ISSN: | 0950-4230 | ||||||||
Official Date: | November 2019 | ||||||||
Dates: |
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Volume: | 62 | ||||||||
Article Number: | 103922 | ||||||||
DOI: | 10.1016/j.jlp.2019.103922 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
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