Abstract As the use of emergency generators for buildings increases, the near-field dispersion of high-temperature and high-buoyancy exhaust gas is becoming a concern. We conducted computational fluid dynamics (CFD) simulations using steady Reynolds-averaged Navier-Stokes (RANS) model and large eddy simulation (LES) on the near-field dispersion of high-buoyancy exhaust gas emitted from a building's wake and validated using a wind tunnel experiment. Realizable k-e 2-layer and wall-adapting local eddy viscosity (WALE) models were used in RANS and LES, respectively. The density difference between the light gas and ambient air in the experiment was reproduced as the temperature difference by an incompressible ideal gas model. RANS model exhibited good agreement with the experimental flow field values for the time-averaged velocity but underestimated the turbulent kinetic energy. LES accurately predicted both the time-averaged velocity and turbulent kinetic energy. For the concentration field, RANS model predicted the region of high time-averaged concentration near the exhaust port but overestimated ground-level values. Meanwhile, LES adequately predicted time-averaged and fluctuating concentrations. Additionally, an investigation of the effect of the turbulent Schmidt number Sct in RANS model demonstrated that a small Sct increased the overall prediction accuracy, while a large Sct compensated for the overestimation on ground-level time-averaged concentrations.

中文翻译：

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使用稳态 RANS 模型和 LES 对孤立立方体建筑物尾流中的高浮力气体扩散进行 CFD 模拟

摘要 随着建筑应急发电机使用量的增加，高温高浮力废气的近场扩散问题日益受到关注。我们使用稳态雷诺平均纳维-斯托克斯 (RANS) 模型和大涡模拟 (LES) 对建筑物尾流排放的高浮力废气的近场扩散进行了计算流体动力学 (CFD) 模拟，并使用风进行了验证隧道实验。Realizable ke 2 层和壁面自适应局部涡粘性 (WALE) 模型分别用于 RANS 和 LES。实验中轻气体与环境空气的密度差通过不可压缩的理想气体模型再现为温差。RANS 模型与实验流场值的时均速度表现出良好的一致性，但低估了湍流动能。LES 准确地预测了时间平均速度和湍流动能。对于浓度场，RANS 模型预测了排气口附近的高时间平均浓度区域，但高估了地面值。同时，LES 充分预测了时间平均和波动的浓度。此外，对 RANS 模型中湍流施密特数 Sct 影响的研究表明，小 Sct 提高了整体预测精度，而大 Sct 补偿了对地面时间平均浓度的高估。LES 准确地预测了时间平均速度和湍流动能。对于浓度场，RANS 模型预测了排气口附近的高时间平均浓度区域，但高估了地面值。同时，LES 充分预测了时间平均和波动的浓度。此外，对 RANS 模型中湍流施密特数 Sct 影响的研究表明，小 Sct 提高了整体预测精度，而大 Sct 补偿了对地面时间平均浓度的高估。LES 准确地预测了时间平均速度和湍流动能。对于浓度场，RANS 模型预测了排气口附近的高时间平均浓度区域，但高估了地面值。同时，LES 充分预测了时间平均和波动的浓度。此外，对 RANS 模型中湍流施密特数 Sct 影响的研究表明，小 Sct 提高了整体预测精度，而大 Sct 补偿了对地面时间平均浓度的高估。