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首页> 外文期刊>Energy >New analytical wake models based on artificial intelligence and rivalling the benchmark full-rotor CFD predictions under both uniform and ABL inflows
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New analytical wake models based on artificial intelligence and rivalling the benchmark full-rotor CFD predictions under both uniform and ABL inflows

机译：在均匀和ABL流入下，基于人工智能的新分析尾流模型可与基准全转子CFD预测相媲美

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摘要

New analytical wake models are derived from the soft computing technique, called Genetic Programming (GP) to predict wake velocities and turbulence intensity. The design of the wind farm's appropriate layout is essential for minimizing cost and maximizing the wind farm power generation. This needs a precise wake velocity model to simulate the wake effect of the wind farm within a limited time duration. Furthermore, prediction of turbulence in the wake due to ambient flow and rotor-generated is extremely crucial owing to its contribution to fatigue loads and structural failures of the downstream wind turbines. This article discusses the classical to the recent analytical wake velocity and turbulence intensity models derived based on hard computing techniques in detail and their limitations. The significant constraints are the consideration of uniform inflow without integrating Atmospheric Boundary Layer (ABL) impacts for the forecast of wake velocity and estimation of single value of turbulence intensity while it radially varies at distinct downstream distances of the wind turbine. Eventually, these constraints are tackled and new analytical models for wake velocity and turbulence intensity profiles are formulated for both uniform and ABL inflows. The existing and proposed models are compared with the previous NREL Phase VI wind turbine CFD study for uniform and ABL inflows and it was observed that the proposed models are precise.
机译：新的分析尾流模型是从称为遗传编程（GP）的软计算技术中得出的，用于预测尾流速度和湍流强度。风电场适当布局的设计对于最小化成本和最大化风电场发电至关重要。这需要精确的苏醒速度模型来模拟风电场在有限时间内的苏醒效果。此外，由于环境流和转子产生的尾流的湍流对疲劳载荷和下游风力涡轮机的结构故障的贡献，因此预测湍流极为重要。本文详细讨论了基于硬计算技术得出的经典到最近的解析苏醒速度和湍流强度模型及其局限性。重要的约束条件是考虑均匀流入，而不考虑大气边界层（ABL）的影响来预测尾流速度和估算湍流强度的单个值，而湍流强度在风力涡轮机的不同下游距离处径向变化。最终，解决了这些限制，并为均匀和ABL流入建立了新的尾流速度和湍流强度曲线分析模型。将现有模型和拟议模型与之前的NREL VI期风力涡轮机CFD研究进行了比较，以了解均匀和ABL流入情况，并观察到拟议模型是精确的。

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  • 来源
    《Energy》 |2020年第15期|116761.1-116761.28|共28页
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  • 作者单位

    School of Mechanical and Aerospace Engineering College of Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore;

    School of Computing Engineering and Mathematics Western Sydney University Penrith NSW 2751 Australia;

    School of Computing Engineering and Mathematics Western Sydney University Penrith NSW 2751 Australia School of Built Environment Western Sydney University Penrith NSW 2751 Australia School of Civil Engineering Hefei University of Technology China;

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  • 正文语种 eng
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  • 关键词

    Genetic programming; Artificial intelligence; Wake velocity models; Wake turbulence intensity models; Wind farm;

    机译：基因编程;人工智能;唤醒速度模型;尾流湍流强度模型;风电场;

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