Reynolds shear stress modeling in turbulent boundary layers subject to very strong favorable pressure gradient
Reynolds shear stress modeling in turbulent boundary layers subject to very strong favorable pressure gradient
复制标题
受非常强的有利压力梯度影响的湍流边界层的雷诺剪应力模型
DOI:
10.1016/j.compfluid.2020.104494
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发表时间:
2020
期刊:
影响因子:
--
通讯作者:
Araya, Guillermo
中科院分区:
文献类型:
--
作者:
Saltar, German;Araya, Guillermo
Recent numerical predictions of turbulent boundary layers subject to very strong Favorable Pressure Gradient (FPG) with high spatial/temporal resolution, ie Direct Numerical Simulation (DNS), have shown a meaningful weakening of the Reynolds shear stresses with a lengthy logarithmic behavior [1, 2]. In the present study, assessment of the Shear Stress Transport and Spalart-Allmaras turbulence models (henceforth SST and SA, respectively) in Reynolds-averaged Navier-Stokes (RANS) simulations is performed. The main objective is to evaluate the ability of popular turbulence models in capturing the characteristic features present during the quasi-laminarization phenomenon in highly accelerating turbulent boundary layers. A favorable pressure gradient is prescribed by a top converging surface (sink flow) with an approximately constant acceleration parameter of K= 4.0× 10− 6. Validation of RANS results is carried out by means of a large DNS dataset [1]. Generally speaking, the SA turbulence model has demonstrated the best compromise between accuracy and quick adaptation to the turbulent inflow conditions. Turbulence models properly captured the increasing trend of the freestream and friction velocity in highly accelerated flows; however, they fail to reproduce the decreasing behavior of the skin friction coefficient, which is typical in early stages of the quasi-laminarization process. Both models have shown deficient predictions of the decreasing and logarithmic behavior of Reynolds shear stresses as well as significantly overpredicted the production of Turbulent Kinetic Energy (TKE) in turbulent boundary layers subject to very strong FPG.
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