Reduced order models for uncertainty quantification of gas plumes from leakages during LNG bunkering. (May 2022)

• Record Type: Journal Article
• Authors: Nguyen, Vinh-Tan; Raghavan, Venugopalan S.G.; Quek, Raymond Y.L.; How, Lim Boon; Yan, Deguang
• Is Part Of: Journal of loss prevention in the process industries. Volume 76(2022)
• Publication Date: 2022-05
• Subjects: Liquefied natural gas -- Jet leak -- Risk analysis -- Computational fluid dynamics -- Uncertainty quantification; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2022.104724
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: The impacts of uncertainty in wind conditions on the spread of hazardous plume resulting from a jet leak during a Liquefied Natural Gas (LNG) bunkering operation were investigated. Computational Fluid Dynamics (CFD) using the Reynolds-Averaged Navier Stokes (RANS) solver with multi-species transport and a transient leak model for keyhole leak was used for the simulation of a simplified bunkering station. Following detailed validation & verification, the sensitivity of the safety zone extents to the wind conditions was demonstrated. CFD results reinforced the strong dependence of the maximum spread distance on wind conditions and enclosure geometry. To quantify the impact of input uncertainty from wind conditions on the plume spread, a reduced-order model (ROM) was developed using the proper orthogonal decomposition (POD) of CFD results on sampled conditions. ROM-POD enables a fast evaluation of the plume under changing wind conditions and acts as an efficient forward model for uncertainty quantification using Polynomial Chaos Expansion (PCE) technique. The spatial distribution of plume residence time under the same input uncertainty was also obtained from the proposed approach showing its potential in risk assessment and design of bunkering facilities. Highlights: A CFD approach for simulations of LNG leaks coupling a RANS solver with a transient leak model. Developing a reduced order model for fast prediction of gas cloud spread. Demonstration of polynomial chaos expansion for uncertainty quantification. Comprehensive numerical investigation of gas cloud spread from LNG leaks with uncertain wind conditions. Promising tool for quantitative risk assessment of LNG facilities given uncertain input parameters.