发表机构
Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过实验将风电场感应区视为逆压梯度湍流边界层问题，发现风电场引起的大尺度湍流增强主要源于逆压梯度，且与转子尺度相当的湍流能量对涡轮疲劳载荷有重要影响。
AI 中文摘要
本文通过热线风速仪，将模型风电场的感应区作为逆压梯度（APG）湍流边界层问题进行了实验研究。采用直径$D=50$毫米的多孔圆盘阵列代表风电场，在两种不同深度的湍流边界层中配置多种布局，并全程比较有风电场与无风电场的情况。风电场在来流中施加了空间发展的逆压梯度。展向平均测量显示，在所有情况下，系统性的风电场尺度整体阻塞效应至少延伸至上游$10D$处。风电场引起的湍流强度增加，在最浅的边界层中相对于无风电场参考值最高达$7\%$，通过累积方差分解表明，这种增加主要由边界层的大尺度运动承载，而小尺度湍流强度仅受到微弱影响。这种选择性的大尺度能量增强与逆压梯度驱动的外层放大一致。风电场引起的湍流能量集中在与转子直径相当或更大的尺度上，这对涡轮疲劳载荷有直接影响，优先激发相干转子尺度载荷波动，其频率在疲劳条件下最具破坏性。研究结果建立了风电场整体阻塞与APG边界层物理之间的定量联系。
英文摘要
The induction region of a model wind farm is investigated experimentally as an adverse pressure gradient (APG) turbulent boundary layer problem, using hot-wire anemometry. An array of porous discs, with diameter $D=50$\,mm representing the farm, in multiple configurations across two turbulent boundary layers of different depths, with farm-present and farm-absent configurations compared throughout. The farm imposes a spatially developing adverse pressure gradient in the approach flow. Spanwise-averaged measurements reveal systematic farm-scale global blockage extending to at least $10D$ upstream in all cases. The farm-induced turbulence intensity increase, which reaches up to $7\%$ relative to the farm-absent reference for the shallowest boundary layer, is shown by cumulative variance decomposition to be carried predominantly by large-scale motions of the boundary layer, whilst the small-scale turbulence intensity is only weakly affected. This selective large-scale energisation is consistent with the outer-layer amplification driven by an adverse pressure gradient. The concentration of farm-induced turbulence energy at scales comparable to or larger than the rotor diameter has direct implications for turbine fatigue loading, preferentially exciting coherent rotor-scale load fluctuations at the frequencies most damaging under fatigue. The results establish a quantitative link between wind farm global blockage and APG boundary layer physics.
Comments16 pages, 9 figures, submitted to FLOW
