Coleman G N, Rumsey C L, Spalart P R
Computational AeroSciences, NASA Langley Research Center, Hampton, VA 23681, USA.
Boeing Commercial Airplanes, Seattle, WA 98124, USA.
J Fluid Mech. 2018 May 17;847:28-70. doi: 10.1017/jfm.2018.257.
A family of cases each containing a small separation bubble is treated by direct numerical simulation (DNS), varying two parameters: the severity of the pressure gradients, generated by suction and blowing across the opposite boundary, and the Reynolds number. Each flow contains a well-developed entry region with essentially zero pressure gradient, and all are adjusted to have the same value for the momentum thickness, extrapolated from the entry region to the centre of the separation bubble. Combined with fully defined boundary conditions this will make comparisons with other simulations and turbulence models rigorous; we present results for a set of eight Reynolds-averaged Navier-Stokes turbulence models. Even though the largest Reynolds number is approximately 5.5 times higher than in a similar DNS study we presented in 1997, the models have difficulties matching the DNS skin friction very closely even in the zero pressure gradient, which complicates their assessment. In the rest of the domain, the separation location is not particularly difficult to predict, and the most definite disagreement between DNS and models is near reattachment. Curiously, the better models tend to cluster together in their predictions of pressure and skin friction even when they deviate from the DNS, although their eddy-viscosity levels are widely different in the outer region near the bubble (or they do not rely on an eddy viscosity). Stratford's square-root law is satisfied by the velocity profiles, both at separation and reattachment. The Reynolds-number range covers a factor of two, with the Reynolds number based on the extrapolated momentum thickness equal to approximately 1500 and 3000. This allows tentative estimates of the improvements that even higher values will bring to the model comparisons. The solutions are used to assess models through pressure, skin friction and other measures; the flow fields are also used to produce effective eddy-viscosity targets for the models, thus guiding turbulence-modelling work in each region of the flow.
对一系列每个都包含一个小分离泡的情况进行了直接数值模拟(DNS),改变了两个参数:由在相对边界上吸气和吹气产生的压力梯度的强度,以及雷诺数。每个流动都包含一个充分发展的入口区域,其压力梯度基本为零,并且所有流动都被调整为具有相同的动量厚度值,该值是从入口区域外推到分离泡中心的。结合完全定义的边界条件,这将使与其他模拟和湍流模型的比较更加严格;我们给出了一组八个雷诺平均纳维 - 斯托克斯湍流模型的结果。尽管最大雷诺数比我们在1997年进行的类似DNS研究中的雷诺数大约高5.5倍,但这些模型即使在零压力梯度下也很难非常精确地匹配DNS表面摩擦力,这使得它们的评估变得复杂。在该区域的其余部分,分离位置并不是特别难以预测,并且DNS与模型之间最明显的分歧出现在再附着附近。奇怪的是,即使这些较好的模型偏离了DNS,它们在压力和表面摩擦力的预测上也倾向于聚集在一起,尽管它们在泡附近的外部区域的涡粘性水平差异很大(或者它们不依赖于涡粘性)。斯特拉特福平方根定律在分离和再附着处的速度剖面上均得到满足。雷诺数范围涵盖了两倍的因子,基于外推动量厚度的雷诺数约为1500和3000。这使得可以初步估计更高的值将给模型比较带来的改进。通过压力、表面摩擦力和其他度量来使用这些解来评估模型;流场还被用于为模型生成有效的涡粘性目标,从而指导流动每个区域的湍流建模工作。