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arXiv 2302.03218physics.flu-dyn
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横流式水轮机性能与流场的周期间变化

Cycle-to-cycle variations in cross-flow turbine performance and flow fields

Abigale Snortland, Isabel Scherl, Brian Polagye, Owen Williams

AI总结:

本文结合二维PIV与同步性能测量,利用PCA加权的无监督层次聚类分析两种工况,揭示横流式水轮机周期间变化主要由来流波动导致动态失速时序变化,而非显著周期间滞后。

摘要:

横流式水轮机的性能和流场表现出周期间变化,尽管这一点常因时间平均和相位平均而被隐含地忽略。这种变化性可能源于多种机制——来流波动、动态失速的随机特性以及周期间滞后——每一种机制对于我们理解横流式水轮机动力学都有不同含义。在本研究中,通过实验在两种截然不同的运行条件下探究了流场和性能的周期间变化程度及来源。流场通过在水轮机扫掠区域内进行二维平面粒子图像测速获得,并与同步测量的性能协同考察。流场与性能变化之间的相关性通过一个无监督的层次化流场聚类流程建立。该流程包含一个主成分分析(PCA)预处理器,使得可以依据高维流场数据中存在的全部动力学,在一个按对整体速度方差的贡献加权、且具有可解释性的低维子空间中进行聚类。我们发现,流场聚类及其相关性能主要与来流波动相关,尽管湍流强度相对较低;这些波动驱动了动态失速过程时序的变化。此外,我们没有发现存在显著周期间滞后的证据。聚类揭示了在水轮机旋转的上游阶段,性能与流场变化之间持续存在的相关性。与聚合的统计表征相比,本文采用的方法更全面地呈现了横流式水轮机流场和性能的图景。

英文摘要:

Cross-flow turbine performance and flow fields exhibit cycle-to-cycle variations, though this is often implicitly neglected through time- and phase-averaging. This variability could potentially arise from a variety of mechanisms -- inflow fluctuations, the stochastic nature of dynamic stall, and cycle-to-cycle hysteresis -- each of which have different implications for our understanding of cross-flow turbine dynamics. In this work, the extent and sources of cycle-to-cycle variability for both the flow fields and performance are explored experimentally under two, contrasting operational conditions. Flow fields, obtained through two-dimensional planar particle image velocimetry inside the turbine swept area, are examined in concert with simultaneously measured performance. Correlations between flow-field and performance variability are established by an unsupervised hierarchical flow-field clustering pipeline. This features a principal component analysis (PCA) pre-processor that allows for clustering based on all the dynamics present in the high-dimensional flow-field data in an interpretable, low-dimensional subspace that is weighted by contribution to overall velocity variance. We find that the flow-field clusters and their associated performance are correlated primarily with inflow fluctuations, despite relatively low turbulence intensity, that drive variations in the timing of the dynamic stall process. Further, we find no evidence of substantial cycle-to-cycle hysteresis. Clustering reveals persistent correlations between performance and flow-field variability during the upstream portion of the turbine rotation. The approach employed here provides a more comprehensive picture of cross-flow turbine flow fields and performance than aggregate, statistical representations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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