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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:工业规模涡轮和相关尾动发展 - 基于Rans的执行器线VS滑动网格接口的比较VS多参考帧方法。

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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 T S R 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 T S R. 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 T S R = 6 and an optimum angle of attack condition at T S R = 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 T S R 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 T S R = 7.5 in the core wake region 0.8 >z/R >0.2 as compared to T S R = 9 and T S R = 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:该当前工作比较了三种方法(致动机线模型(ALM),滑动网格接口(SMI)和多个参考帧(MRF))在不同尖端速度比(TSR)以不同的尖端速度比(TSR)建模。比较表明,所有3个型号都以围绕TSR≈7.5的最佳方式预测功率系数(CP)VS T S R曲线的预期趋势。但是预测CP的定量值,唤醒缺陷和流模式不同于模型到模型。在ALM和MRF之间,前者预测CP与T S R的相对较高的变化。对涡轮机后面的流动模式和唤醒缺陷的更深分析有助于了解这些模型的行为特征。 MRF表示具有TSR的流动模式的变化,如T S R = 6的相关的失速条件,以及T S R = 7.5的最佳攻角。 ALM仅显示在不同TSR的集线器区域(DU40位置)附近的流动模式的微小变化。这是因为刀片在ALM中没有解决。也许,流量模式预测的这种差异导致MRF和ALM模型预测的CP VS T S趋势的差异。另一方面,SMI捕获复杂的3D流动结构。唤醒缺陷比较表明,与TSR = 9和TSR = 6相比,ALM和MRF模型在核心唤醒区域0.8> Z / R> 0.2中捕获TSR = 7.5的定性较高的唤醒缺陷。此行为也与之相关观察到这些模型捕获的流动模式。未来的研究可能涉及使用ALM中的LES看它是否改善了RAN预测。

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