为了分析近壁面处理方法对湍流数值计算的影响,应用几种不同的壁面处理方法,对长直圆管管内湍流进行了模拟计算,并将计算结果与试验数据进行对比。结果表明:壁面网格尺寸的选取对湍流计算结果具有较大的影响,且Viscous Grid Space Calcu...为了分析近壁面处理方法对湍流数值计算的影响,应用几种不同的壁面处理方法,对长直圆管管内湍流进行了模拟计算,并将计算结果与试验数据进行对比。结果表明:壁面网格尺寸的选取对湍流计算结果具有较大的影响,且Viscous Grid Space Calculator给出的壁面网格尺寸具有较好的应用性;壁面函数法在高雷诺数区域具有较高的计算精度,而低雷诺数湍流模型可较精确地模拟出近壁区的速度分布,其中Spalart-Allmaras湍流模型的计算结果与试验数据吻合得最好,该模型在湍流计算中具有较大的优势。展开更多
The predictive capability of Reynolds-averaged numerical simulation （RANS） models is investigated by simulating the flow in meandering open channel flumes and comparing the obtained results with the measured data. T...The predictive capability of Reynolds-averaged numerical simulation （RANS） models is investigated by simulating the flow in meandering open channel flumes and comparing the obtained results with the measured data. The flow structures of the two experiments are much different in order to get better insights. Two eddy viscosity turbulence models and different wall treatment methods are tested. Comparisons show that no essential difference exists among the predictions. The difference of turbulence models has a limited effect, and the near wall refinement improves the predictions slightly. Results show that, while the longitudinal velo- cities are generally well predicted, the predictive capability of the secondary flow is largely determined by the complexity of the flow structure. In Case 1 of a simple flow structure, the secondary flow velocity is reasonably predicted. In Case 2, consisting of sharp curved consecutive reverse bends, the flow structure becomes complex after the first bend, and the complex flow structure leads to the poor prediction of the secondary flow. The analysis shows that the high level of turbulence anisotropy is related with the boundary layer separation, but not with the flow structure complexity in the central area which definitely causes the poor prediction of RANS models. The turbulence model modifications and the wall treatment methods barely improve the predictive capability of RANS models in simulating complex flow structures.展开更多
文摘为了分析近壁面处理方法对湍流数值计算的影响,应用几种不同的壁面处理方法,对长直圆管管内湍流进行了模拟计算,并将计算结果与试验数据进行对比。结果表明:壁面网格尺寸的选取对湍流计算结果具有较大的影响,且Viscous Grid Space Calculator给出的壁面网格尺寸具有较好的应用性;壁面函数法在高雷诺数区域具有较高的计算精度,而低雷诺数湍流模型可较精确地模拟出近壁区的速度分布,其中Spalart-Allmaras湍流模型的计算结果与试验数据吻合得最好,该模型在湍流计算中具有较大的优势。
基金Project supported by the National Basic Research Deve-lopment Program of China(973 Program,Grant No.2012CB417002)the National Science-Technology Support Plan Projects(Grant Nos.2012BAB05B01,2012BAB04B03)+1 种基金the State Key Program of National Natural Science of China(Grant No.51039004)the National Natural Science Foundation of China(Grant No.51579151)
文摘The predictive capability of Reynolds-averaged numerical simulation （RANS） models is investigated by simulating the flow in meandering open channel flumes and comparing the obtained results with the measured data. The flow structures of the two experiments are much different in order to get better insights. Two eddy viscosity turbulence models and different wall treatment methods are tested. Comparisons show that no essential difference exists among the predictions. The difference of turbulence models has a limited effect, and the near wall refinement improves the predictions slightly. Results show that, while the longitudinal velo- cities are generally well predicted, the predictive capability of the secondary flow is largely determined by the complexity of the flow structure. In Case 1 of a simple flow structure, the secondary flow velocity is reasonably predicted. In Case 2, consisting of sharp curved consecutive reverse bends, the flow structure becomes complex after the first bend, and the complex flow structure leads to the poor prediction of the secondary flow. The analysis shows that the high level of turbulence anisotropy is related with the boundary layer separation, but not with the flow structure complexity in the central area which definitely causes the poor prediction of RANS models. The turbulence model modifications and the wall treatment methods barely improve the predictive capability of RANS models in simulating complex flow structures.
