Simulation of heavy gas dispersion in a large indoor space using CFD model
Simulation of heavy gas dispersion in a large indoor space using CFD model
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DOI：
10.1016/j.jlp.2017.01.012
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发表时间：
2017-03-01
影响因子：
3.5
通讯作者：
Wu, Liyang
中科院分区：
文献类型：
--
作者：
Dong, Longxiang;Zuo, Hongchao;Wu, Liyang
准确预测大型室内环境中意外泄漏所造成的相关危害对于风险分析和应急响应至关重要。虽然计算流体动力学（CFD）被视为模拟复杂扩散情景的有前途的工具，但在预测可以有信心地使用之前，有必要根据可靠的实验数据对其进行充分验证。本文对四种常用的雷诺平均Navier-Stokes（RANS）湍流模型（即，标准k-ε、Realizable k-ε、标准k-ω和剪切应力输运（SST）k-ω），用于预测具有各种障碍物的厂房中的重质气体扩散。预测的浓度进行了比较与示踪剂（SF6）的浓度测定从一个全面的室内示踪剂测试，其中14个采样点分布在释放源周围。应用几种统计测量来量化呼吸区内和呼吸区以上的模型性能。结果表明，标准k-w模型和SST k-co模型在呼吸区表现出最好的性能，根据张的标准，产生78%的预测（配对的时间和空间）内的两个因素的观察。然而，没有一个RANS模式是令人满意的预测呼吸区上方的示踪剂云的时空变化。此外，SST的k-w模型再现了重气体在建筑物中的扩散，并分析了通风量和呼吸区浓度之间的关系。(C)2017作者由Elsevier Ltd.发布。这是CC BY-NC-ND许可证下的开放获取文章（http：//creativecommons.orgflicensesiby-nc-nd/4.0/）。
Accurate prediction of the associated hazards resulting from accidental leakage in a large indoor environment is essential for risk analysis and emergency response. Although computational fluid dynamics (CFD) is seen as a promising tool to model' complex dispersion scenarios, it is necessary that it be fully validated against reliable experimental data before the predictions can be used with confidence. This paper presents a comprehensive and systematic evaluation of the performance of four commonly used Reynolds Averaged Navier-Stokes (RANS) turbulence models (i.e., standard k-epsilon, Realizable k-epsilon, standard k-w, and shear-stress transport (SST) k-co) for predicting heavy gas dispersion in a factory building with various obstacles. The predicted concentrations were compared with tracer (SF6) concentrations measured from a full-scale indoor tracer test, in which 14 sampling sites were distributed around the release source. Several statistical measures were applied to quantify the model performance in and above the breathing zone. The results suggest that the standard k-w model and the SST k-co model show the best performance in the breathing zone according to Chang's criteria, producing 78% of predictions (paired in time and space) within a factor of two of the observations. However, none of the RANS models are satisfactory in predicting the spatial-temporal variation of tracer clouds above the breathing zone. Additionally, the SST k-w model was employed to reproduce the heavy gas dispersion in the building and to analyze the relationship between ventilation rate and concentration in the breathing zone. (C) 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.orgflicensesiby-nc-nd/4.0/).