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ON THE IDENTIFICATION AND DECOMPOSITION OF THE UNSTEADY LOSSES IN A TURBINE CASCADE

机译:汽轮机中非定常损失的识别与分解

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The present paper describes the application of Proper Orthogonal Decomposition (POD) to Large Eddy Simulation (LES) of the T106A low-pressure-turbine profile with unsteady incoming wakes at two different flow conditions. Conventional data analysis applied to time averaged or phase-locked averaged flow fields is not always able to identify and quantify the different sources of losses in the unsteady flow field as they are able to isolate only the deterministic contribution. A newly developed procedure allows such identification of the unsteady loss contribution due to the migration of the incoming wakes, as well as to construct reduced order models able to highlight unsteady losses due to larger and/or smaller flow structures carried by the wakes in the different parts of the blade boundary layers. This enables a designer to identify the dominant modes (i.e. phenomena) responsible for loss, the associated generation mechanism, their dynamics and spatial location. The procedure applied to the two cases shows that losses in the fore part of the blade suction side are basically unaffected by the flow unsteadiness, irrespective of the reduced frequency and the flow coefficient. On the other hand, in the rear part of the suction side the unsteadiness contributes to losses prevalently due to the finer scale (higher order POD modes) embedded into the bulk of the incoming wake. The main difference between the two cases has been identified by the losses produced in the core flow region, where both the largest scale structures and the finer ones produces turbulence during migration. The decomposition into POD modes allows the quantification of this latter extra losses generated in the core flow region, providing further inputs to the designers for future optimization strategies.
机译:本文介绍了在两种不同的流动条件下,具有不定常传入尾流的T106A低压涡轮轮廓的大涡模拟(LES)的正确正交分解(POD)的应用。应用于时间平均或锁相平均流场的常规数据分析并不总是能够识别和量化非稳定流场中的不同损失源,因为它们只能隔离确定性贡献。新开发的程序可以对由于进入尾流的迁移而引起的非稳态损失的这种识别进行识别,并构建降阶模型,该模型能够突出由于尾流在不同位置携带的较大和/或较小的流动结构而引起的非稳态损失。叶片边界层的一部分。这使设计人员能够确定造成损失的主要模式(即现象),相关的生成机制,其动态性和空间位置。应用于这两种情况的过程表明,叶片吸力侧前部的损失基本上不受流动不稳定性的影响,而与降低的频率和流动系数无关。另一方面,在吸力侧的后部,由于嵌入到传入尾流的主体中的更细的水垢(高阶POD模式),不稳定导致了损失。两种情况之间的主要区别已由岩心流动区域中产生的损失确定,在岩心流动区域中,最大规模的结构和较细的结构在迁移过程中都会产生湍流。分解为POD模式可量化后一部分在核心流区域产生的额外损失,从而为设计人员提供进一步的输入,以供将来的优化策略使用。

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