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.

研究了风条件的不确定性对液化天然气 (LNG) 加注作业期间喷射泄漏导致的有害羽流扩散的影响。计算流体动力学 (CFD) 使用雷诺平均 Navier Stokes (RANS) 求解器与多物质传输和用于钥匙孔泄漏的瞬态泄漏模型用于模拟简化的加油站。经过详细的验证和验证，证明了安全区范围对风况的敏感性。CFD 结果强化了最大传播距离对风力条件和外壳几何形状的强烈依赖性。为了量化来自风条件的输入不确定性对羽流扩散的影响，在采样条件下，使用 CFD 结果的适当正交分解 (POD) 开发了降阶模型 (ROM)。ROM-POD 能够在不断变化的风条件下快速评估羽流，并作为使用多项式混沌扩展 (PCE) 技术进行不确定性量化的有效前向模型。相同输入不确定性下羽流停留时间的空间分布也从所提出的方法中获得，表明其在风险评估和加油设施设计中的潜力。