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Direct numerical simulations of turbulent thermal boundary layers subjected to adverse streamwise pressure gradients

机译:逆流向压力梯度作用下湍流热边界层的直接数值模拟

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摘要

An innovative method for prescribing turbulent thermal inflow information in spatially developing boundary layers under streamwise pressure gradients is introduced for attached flows. The approach is tested and validated in a suite of Direct Numerical Simulations (DNS) of thermal boundary layers for zero (ZPG) and adverse (APG) pressure gradients with momentum thickness Reynolds numbers (Reθ) up to 3000. The turbulent thermal data are generated based on the dynamic multi-scale approach proposed by Araya et al. ["A dynamic multi-scale approach for turbulent inflow boundary conditions in spatially evolving flows," J. Fluid Mech.670, 581-605 (2011)], which is extended to include thermal field simulations in the present article. The approach is based on the original rescaling-recycling method developed by Lund, Wu, and Squires ["Generation of turbulent inflow data for spatially developing boundary layer simulations," J. Comput. Phys.140, 233-258 (1998)] for ZPG flows. Isothermal walls are considered for the thermal field and the molecular Prandtl number is 0.71. In addition, only inlet momentum/thermal boundary layer thicknesses must be prescribed while other flow parameters such as the inlet friction velocity, uτ, and friction temperature, Θτ, are computed dynamically based on the flow solution obtained downstream by means of a test plane. This plane is located between the inlet and recycle stations. Based on the unique and extensive DNS results of heat transfer obtained in this investigation, the effects of Reynolds numbers and adverse pressure gradients on the flow and thermal parameters are also explored and visualized. The principal outcome of adverse pressure gradient on the flow parameters has been determined as a secondary peak, particularly on the streamwise velocity fluctuations in the outer region, which shows clear evidence of energy production in the outer flow and not only in the buffer layer as traditionally known. Nevertheless, this peak is not so obvious on the thermal fluctuations but it is hypothesized that the reason is mainly attributed to the absence of a freestream thermal gradient, as imposed in the velocity field. Furthermore, the high-speed streaks in the buffer layer are observed to be notably shorter and wider in a Strong APG than in the ZPG case. Finally, a significant decrease of the turbulent Prandtl number is attributed to the presence of a Strong APG.
机译:提出了一种创新的方法,用于在流向压力梯度下在空间扩展的边界层中规定湍流热流入信息,以用于附加流。在热边界层的直接数值模拟(DNS)套件中对零(ZPG)和逆(APG)压力梯度(动量厚度雷诺数(Reθ)高达3000)进行了测试和验证。生成了湍流热数据基于Araya等人提出的动态多尺度方法。 [“用于动态演化的多尺度方法,用于空间演化流中的湍流入流边界条件,” J。Fluid Mech.670,581-605(2011)],在本文中扩展为包括热场模拟。该方法基于由Lund,Wu和Squires [J. Comput。的“为空间展开的边界层模拟生成湍流流入数据”的原始比例缩放回收方法。 ZPG流程,[Phys.140,233-258(1998)]。等温壁被认为是热场,其分子普朗特数为0.71。另外,仅必须规定入口动量/热边界层的厚度,而其他流动参数(例如入口摩擦速度uτ和摩擦温度Θτ)则是基于在下游通过测试平面获得的流动解而动态计算的。该平面位于入口站和回收站之间。根据在这项研究中获得的独特且广泛的传热DNS结果,还探索和可视化了雷诺数和不利的压力梯度对流量和热参数的影响。不利的压力梯度对流量参数的主要结果已确定为次要峰,特别是在外部区域的水流速度波动上,这显示了在外部流体中产生能量的明显证据,而不仅是传统上的缓冲层众所周知。然而,这个峰值在热波动上不是很明显,但据推测,其原因主要归因于缺乏在速度场中施加的自由流热梯度。此外,观察到在强APG中,与ZPG情况相比,缓冲层中的高速条纹明显更短和更宽。最后,湍流普朗特数的显着降低归因于强APG的存在。

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