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Industrial scale turbine and associated wake development -comparison of RANS based Actuator Line Vs Sliding Mesh Interface Vs Multiple Reference Frame method

机译:工业规模涡轮机及相关尾流发展 - 基于RaNs的执行器线路Vs滑动网格接口与多参考框架方法的比较

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摘要

This current work compares the three methodologies (Actuator Line model (ALM), Sliding Mesh Interface (SMI) and Multiple Reference Frame (MRF)) in modeling an industrial scale reference turbine at different tip speed ratios (TSR). The comparison shows that all the 3 models qualitatively predict the expected trend of power coefficient (Cp) vs TSR curve with an optima around TSR ≈ 7.5. But the quantitative values of the predicted Cp, the wake deficits and the flow patterns differ from model to model. Between ALM and MRF, the former predicts a relatively milder variation of Cp with TSR. A deeper analysis of the flow pattern and wake deficit behind the turbine helps in understanding the behavioral characteristics of these models. MRF shows variations in flow pattern with TSRs, like the associated stall conditions at TSR = 6 and an optimum angle of attack condition at TSR = 7.5. ALM shows only a slight variation in the flow pattern near the hub region (DU40 location) at different TSRs. This is because the blades are not resolved in ALM. Perhaps, such differences in flow-pattern predictions result in the differences in Cp vs TSR trends predicted by the MRF and ALM models. SMI, on the other hand, captures the complex 3D flow structures. The wake deficit comparison shows that both ALM and MRF model captures qualitatively higher wake deficit at TSR = 7.5 in the core wake region 0.8 > z/R > 0.2 as compared to TSR = 9 and TSR = 6. This behavior too is related to the observed flow pattern as captured by these models. Future studies may involve using LES in ALM to see if it improves the RANS predictions.
机译:本工作比较了在以不同的叶尖速比(TSR)建模工业规模参考涡轮时,对三种方法(执行器线模型(ALM),滑动网格接口(SMI)和多参考框架(MRF))进行比较。比较表明,所有3个模型都定性地预测了功率系数(Cp)与TSR曲线的预期趋势,并且在TSR≈7.5附近具有最佳值。但是,预测的Cp,尾流不足和流动模式的定量值因模型而异。在ALM和MRF之间,前者预测TSR会导致Cp的变化相对温和。对涡轮后面的流型和尾流不足进行更深入的分析有助于理解这些模型的行为特征。 MRF显示了TSR的流型变化,例如在TSR = 6时相关的失速条件和在TSR = 7.5时的最佳迎角条件。 ALM在不同TSR的中心区域(DU40位置)附近仅显示出流型的细微变化。这是因为刀片无法在ALM中解析。也许,这种流模式预测的差异会导致MRF和ALM模型所预测的Cp与TSR趋势的差异。另一方面,SMI捕获复杂的3D流结构。尾流不足比较显示,与TSR = 9和TSR = 6相比,ALM和MRF模型在核心尾随区域0.8> z / R> 0.2中在TSR = 7.5时都捕获到了较高的尾流缺陷。这些模型捕获的观察到的流动模式。未来的研究可能涉及在ALM中使用LES,以查看它是否可以改善RANS预测。

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