论文标题

使用电网的低压涡轮刀片上的分离和过渡预测

Prediction of separation and transition on a low-pressure turbine blade using a RANS grid

论文作者

Ranjan, Rajesh, Deshpande, S. M., Narasimha, Roddam

论文摘要

由于经常涉及诸如分离和过渡之类的现象,因此流过高升的低压涡轮(LPT)叶片可能非常复杂。对于高发射率和相对较低的雷诺数(25,000 <re <1,00,000)的高度负载的T106A刀片,在刀片的吸入侧观察到了分离引起的过渡,这是基于模型的模拟的挑战性问题。在这项工作中,使用RANS和混合LES/RANS方法进行了该流量的计算。 RANS模拟使用六个流行的低湍流模型进行。虽然湍流模型本身无法预测T106A刀片上的任何分离,但四个方程式朗特门室过渡模型预测了一个短的分离气泡。但是,这种气泡的特征与实验和DNS中观察到的特征大不相同,因此无法准确预测过渡。然后使用嵌入式混合LES/RANS方法,有限的数值尺度(LNS),并在足够解析的网格中自动开关向LES进行自动开关,然后用于对Samerans网格的预测。随着细网格的统计湍流,LNS的类似LES样行为导致雷诺应力的非物理下降,因为湍流波动在分辨率的尺度上未适当地表示。因此,LNS结果与湍流模型获得的结果非常相似。但是,当使用具有正确统计特征的合成湍流来刺激嵌入式LES区域中的大涡流时,LNS能够预测分离并恢复非常接近DNS和实验结果的溶液。

Flow past a high-lift low-pressure turbine (LPT) blade in a cascade could be quite complex as phenomena like separation and transition are often involved. For a highly loadedT106A blade at a high incidence and relatively low Reynolds number(25, 000 < Re < 1, 00, 000), separation-induced transition is observed on the suction side of the blade, making it a challenging problem for model-based simulations. In this work, computations for this flow are carried out using RANS and hybrid LES/RANS approaches. The RANS simulations are performed with six popular low- Re turbulence models. While turbulence models by themselves fail to predict any separation on the T106A blade, the four-equation Langtry-Menter transition model predicts a short separation bubble. The characteristic of this bubble, however, is very different from what is observed in experiments and DNS, and therefore transition is not accurately predicted. An embedded hybrid LES/RANS approach, Limited numerical scales(LNS), with an automatic switch to LES in sufficiently resolved grids, is then used for predictions on the sameRANS grid. With the statistical turbulence on fine grids, LES-like behavior of LNS results in an unphysical drop in Reynolds stresses as the turbulent fluctuations are not appropriately represented on the resolved scale. Therefore, the LNS results are very similar to those obtained with turbulence models. However, when synthetic turbulence with correct statistical characteristics is used to stimulate the large eddies in the embedded LES zone, LNS is able to predict separation and recovers a solution very close to DNS and experimental results.

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