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Identification and quantification of losses in a LPT cascade by POD applied to LES data

机译:通过POD识别和量化LPT级联中的损耗,并将其应用于LES数据

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A POD based procedure has been developed to identify and account for the different contributions to the entropy production rate caused by the unsteady aerodynamics of a low-pressure (LP) turbine blade. LES data of the extensively studied T1 06A cascade have been used to clearly highlight the capability of POD to identify deterministic incoming wake related modes, stochastic fine-scale structures embedded within the bulk of the wake carried during migration, and coherent structures originating in the boundary layer as a consequence of the wake-boundary layer interaction process. The POD modes computed by a kinematic kernel generate a full and complete basis, where both the velocity and enthalpy fields have been projected through an extended POD procedure to determine the relative coefficients. This allows to separately compute orthogonal sets of contributions to turbulent kinetic energy production, enthalpy-velocity correlation and turbulent dissipation of resolved structures, thus clearly identifying the dominating modes (i.e. phenomena) responsible for the overall entropy production rate. Moreover, low-order truncation of these different contributions have been grouped into three different parts: those arising from the deterministic incoming wake, those due to the turbulence carried by the wakes and its interaction with the boundary layer, and those related to boundary layer events. The spatial integration of these low-order truncations restricted to the time-mean boundary layer, wake mixing and the potential flow regions of the blade passage allows gathering further information on the unsteady loss generation mechanisms, and where they mainly act. Particularly, results show that the procedure is able to decompose losses into the dominant contributions, thus providing a new tool for a rapid and clear identification of the different sources of losses in complex unsteady flow fields.
机译:已经开发了一种基于POD的程序,以识别和说明由低压(LP)涡轮叶片的不稳定空气动力学引起的对熵生产率的不同贡献。广泛研究的T1 06A级联的LES数据已被用来清楚地强调POD识别确定性传入尾流相关模式,在迁移过程中嵌入的大量尾流中嵌入的随机精细尺度结构以及源自边界的相干结构的能力。尾边界层交互过程的结果。运动内核计算的POD模式产生了完整的基础,其中速度场和焓场都已通过扩展的POD程序进行投影以确定相对系数。这允许分别计算对湍动能产生,焓-速度相关性和解析结构的湍流耗散的正交贡献集,从而清楚地确定造成整体熵产生率的主导模式(即现象)。此外,这些不同贡献的低阶截断被分为三个不同部分:确定性传入尾流引起的那些,尾流所携带的湍流及其与边界层的相互作用引起的那些以及与边界层事件有关的那些。这些低阶截断的空间整合仅限于时间平均边界层,尾流混合和叶片通道的潜在流动区域,从而可以收集有关不稳定损失产生机理及其主要作用部位的进一步信息。特别是,结果表明,该程序能够将损失分解为主要贡献,从而为在复杂的非恒定流场中快速,清晰地识别损失的不同来源提供了一种新工具。

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