Evaluation of various turbulence models in predicting airflow and turbulence in enclosed environments by CFD: Part 2-comparison with experimental data from literature
Evaluation of various turbulence models in predicting airflow and turbulence in enclosed environments by CFD: Part 2-comparison with experimental data from literature
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DOI：
10.1080/10789669.2007.10391460
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发表时间：
2007-11-01
期刊：
影响因子：
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通讯作者：
Chen, Qingyan (Yan)
中科院分区：
文献类型：
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作者：
Zhang, Zhao;Zhai, Zhiqiang (John);Chen, Qingyan (Yan)
在过去的二十年里，已经发展了许多湍流模型，其中许多可以用来预测封闭环境中的气流和湍流。评价各种室内气流情况下湍流模型的通用性和稳健性是非常重要的。本研究从精度和计算成本两方面对八种可能适用于室内气流的湍流模型的性能进行了评估。这些模型涵盖了广泛的计算流体力学(CFD)方法，包括雷诺平均Navier-Stokes(RANS)模型、混合RANS和大涡模拟(或分离涡模拟[DES])和大涡模拟(LES)。所测试的RANS湍流模型包括室内零方程模型、三个两方程模型(RNG k-epsilon、低雷诺数k-epsilon和SST k-omega模型)、一个三方程模型(nu(2)-f模型)和一个雷诺应力模型(RSM)。研究测试了这些模型在封闭环境中的代表性流动，如通风空间中的强迫对流和混合对流，高空腔中具有中等温度梯度的自然对流，以及模型火灾房间中具有大温度梯度的自然对流。将模型预测的风速、温度、雷诺应力和湍流热通量与文献中的实验数据进行了比较。这项研究还比较了每种模型在所有情况下使用的计算时间。结果表明，大涡模拟提供了最详细的流动特征，但计算时间比RANS模型高得多，精度也不一定是最高的。在所研究的RANS模型中，RNG k-epsilon和改进的v(2)-f模型在所研究的四个案例中总体表现最好。同时，其他型号只有在特定情况下才有更好的性能。虽然每种湍流模型在某些流动类别中都有很好的精度，但每种流动类型支持不同的湍流模型。因此，我们在结论和建议中总结了每种特定模型在不同流动中的性能以及最适合每种流动类别的湍流模型。
Numerous turbulence models have been developed in the past two decades, and many of them can be used in predicting airflows and turbulence in enclosed environments. It is important to evaluate the generality and robustness of the turbulence models for various indoor airflow scenarios. This study evaluated the performance of eight turbulence models, potentially suitable for indoor airflow, in terms of accuracy and computing cost. These models cover a wide range of computational fluid dynamics (CFD) approaches, including Reynolds averaged Navier-Stokes (RANS) modeling, hybrid RANS and large-eddy simulation (or detached-eddy simulation [DES]), and large-eddy simulation (LES). The RANS turbulence models tested include the indoor zero-equation model, three two-equation models (the RNG k-epsilon, low Reynolds number k-epsilon, and SST k-omega models), a three-equation model (nu(2)-f model), and a Reynolds-stress model (RSM). The investigation tested these models for representative in flows in enclosed environments, such as forced convection and mixed convection in ventilated spaces, natural convection with medium temperature gradient in a tall cavity, and natural convection with large temperature gradient in a model fire room. The air velocity, air temperature, Reynolds stresses, and turbulent heat fluxes predicted by the models were compared against the experimental data from the literature. The study also compared the computing time used by each model for all cases. The results reveal that LES provides the most detailed flow features, while the computing time is much higher than for RANS models, and the accuracy may not always be the highest. Among the RANS models studied, the RNG k-epsilon and a modified v(2)-f model perform the best overall in four cases studied. Meanwhile, the other models have superior performance only in particular cases. While each turbulence model has good accuracy in certain flow categories, each flow type favors different turbulence models. Therefore, we summarize in the conclusions and recommendations both the performance of each particular model in different flows and the best suited turbulence models for each flow category.