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IMPROVED PREDICTION OF LABYRINTH SEAL PERFORMANCE THROUGH SCALE ADAPTIVE SIMULATION AND STREAM ALIGNED GRIDS

机译:通过规模自适应模拟和流对齐网格改进了拉贝林密封性能的预测

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The accurate prediction of cavity flows is of importance to the turbomachinery design process. However, cavity flows are complex. It is known, that RANS models tend to struggle with the prediction of cavity flows and the flow phenomena associated with them. At the same time, scale-resolving methods are more accurate and give a more detailed view on the turbulent structure of the flow. This is accompanied by an inherent dependency on the computational grid, the timestep, and the size of the domain. Therefore, an experimentally validated comparison of RANS, URANS and SAS simulations for a stepped labyrinth seal is given in the paper at hand to demonstrate the individual methods capabilities, limitations, and requirements. It was shown that an alignment of the grid with the local flow direction can save about 40% of computational resources, while simultaneously reducing the discretization error by 25%. RANS and time averaged URANS results in comparison to measurements showed that the swirl development in the cavity is overpredicted and the cavity vortex is underpredicted. A distinct grid dependency was noticed for the SAS-SST turbulence model. The intermediate grid enhances the results in comparison to RANS and URANS. URANS-SST and SAS-SST simulations capture the same dominant frequencies of the velocity spectra, when the same sector size is used. Furthermore, the prediction of dominant frequencies depends strongly on the circumferential size of the domain. The time-averaged results are more sensitive to the grid refinement and turbulence model than to the size of the domain.
机译:腔流量的准确预测对涡轮机械设计过程至关重要。然而,腔流是复杂的。众所周知,RANS模型往往难以预测空腔流动以及与之相关的流动现象。同时,水垢解析方法更准确,并且可以更详细地了解流的湍流结构。这伴随着对计算网格,时间步长和域大小的内在依赖。因此,本文中对阶梯迷宫式密封进行了RANS,URANS和SAS仿真的实验验证比较,以证明各个方法的功能,局限性和要求。结果表明,网格与局部流向对齐可以节省大约40%的计算资源,同时将离散化误差降低25%。 RANS和时间平均URANS结果与测量结果相比,表明空腔中的涡旋发展被高估了,而空腔涡旋却被低估了。注意到SAS-SST湍流模型具有明显的网格依赖性。与RANS和URANS相比,中间网格增强了结果。当使用相同的扇区大小时,URANS-SST和SAS-SST模拟会捕获相同的速度谱主频。此外,主导频率的预测在很大程度上取决于域的圆周尺寸。时间平均结果对网格细化和湍流模型比对域的大小更敏感。

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