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Characterization of non-isothermal flows typical of built environments in a laboratory scale model. Part Ⅱ - Numerical predictions with CFD

机译:在实验室规模的模型中,典型的非等温流动是建筑环境的特征。第二部分-CFD的数值预测

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CFD predictions of built environment flows require turbulence modelling. RANS-based turbulence models (standard and RNG k-ε) have been widely used due to their ability to capture the main features of the time-averaged variables of such flows without demanding unaffordable computer requirements. Herein, the experimental results presented in Part I are used to validate numerical predictions of non-isothermal flows performed with the standard, the RNG with turbulent viscosity formulation and, for the first time, the RNG with differential effective viscosity formulation k-ε models. Two office geometries are simulated for mixing and displacement ventilation strategies. For mixing the RNG k-ε model with the differential effective viscosity provides the most accurate results for both U and V velocity components, temperature, turbulence intensity and PPD comfort index. However, the W velocity component and the DR comfort index are better predicted by the high-Reynolds number models. For displacement there is not a remarkable improvement in results of the velocity, temperature, turbulence intensity and comfort indexes generated by the RNG k-ε with the differential effective viscosity calculation, when compared with those obtained with the turbulent viscosity formulation. Both models yield much more accurate results for temperature and velocity than those given by the standard k-ε model. In opposition, the former yields the best results for the DR comfort index. From the analysis of the turbulence intensity profiles for both ventilation strategies it is possible to infer the need to improve the buoyancy model when using low-Reynolds number turbulence models.
机译:建筑物环境流的CFD预测需要湍流建模。基于RANS的湍流模型(标准和RNGk-ε)已被广泛使用,因为它们能够捕获此类流的时间平均变量的主要特征,而又不需要昂贵的计算机需求。本文中,第一部分中提供的实验结果用于验证使用标准品,采用湍流粘度公式的RNG和采用差分有效粘度公式k-ε模型的RNG进行的非等温流动的数值预测。针对混合和置换通风策略,模拟了两个办公室的几何形状。通过将RNGk-ε模型与有效差分粘度混合,可提供关于U和V速度分量,温度,湍流强度和PPD舒适度指数的最准确结果。但是,高雷诺数模型可以更好地预测W速度分量和DR舒适度指数。对于位移,与使用湍流粘度配方获得的结果相比,通过差分有效粘度计算得出的RNGk-ε所产生的速度,温度,湍流强度和舒适度指标的结果没有显着改善。与标准k-ε模型给出的结果相比,这两种模型均能获得更精确的温度和速度结果。相反,前者对DR舒适度指数的效果最好。从两种通风策略的湍流强度分布图分析,可以推断出在使用低雷诺数湍流模型时需要改进浮力模型。

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