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Accurate modelling of wind turbine wake spreading through consideration of realistic turbulent entrainment: revolutionising wind farm optimisation

通过考虑现实湍流夹带对风力涡轮机尾流传播进行精确建模：彻底改变风电场优化

基本信息

批准号：
EP/V006436/1
负责人：
Oliver Buxton
金额：
$ 164.39万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV006436%2F1
关键词：
Accurate modelling wind turbine wake

项目摘要

Wind energy currently produces 18% of the UK's power but, in a drive towards a de-carbonised economy by 2050, this proportion must increase substantially over the next decade. The UK government has committed to increase offshore wind power capacity by 1-2 GW per year until 2030, reflecting the fact that the country contains some of the best locations for offshore wind in Europe. As the UK becomes more reliant upon wind energy, it is of increasing importance to improve both the efficiency and reliability of wind farms. Since wind turbines which lie in the wakes of upstream machines produce less power and experience higher fatigue loading than those upstream, there is scope to achieve this goal by improving our ability to predict the wakes generated by wind turbines and thereby design an optimally laid out wind farm given knowledge of the prevailing wind conditions. Our ability to optimise wind farms is currently hampered by an over-reliance on out-of-date empiricism. This proposal seeks to rectify this by developing physics-based modelling tools to better describe individual wind-turbine wakes as well as the interactions between interacting wakes within a wind farm. Offshore wind farms are particularly amenable to optimisation due to the stability of the prevailing wind conditions in comparison to onshore sites.Optimal spacing of wind turbines revolves around several factors. These are the desire to produce as much power as possible from a given site whilst at the same time minimising maintenance costs in response to fatigue damage caused by turbines sitting in the highly unsteady, turbulent wake of an upstream machine. This requires confident prediction of the spreading of wind turbine wakes plus a methodology to estimate the fatigue lifetime of wind turbine components in response to their predicted inflow conditions. In addition, there is the problem of predicting the global blockage in which the wind farm as a whole has the effect of diverting the wind over/around the wind farm meaning that the true inflow wind speed to the farm is not the same as the prevailing wind. Specifically, we will:1. Perform innovative experiments in order to better understand the flow physics underpinning the spreading of turbulent wakes. This will involve exploring the interactions in the near wake between the coherence introduced at multiple length scales simultaneously by, for example, the tower, nacelle and blade-tip vortices. In addition we will explore the physics behind the spreading of the produced wake due to the phenomenon of entrainment, which is the process by which mass/energy is transferred from the background into the wake. In particular we will focus on the effect of atmospheric, and wake, turbulence on entrainment.2. Take this new physical understanding and translate it into a physics-based model for the spreading of an individual wind-turbine wake.3. Devise a methodology to make accurate predictions for the fatigue lifetime of vulnerable wind-turbine components (e.g. the gear box/trailing edge bond etc.) in response to the fluctuating inflow caused by atmospheric/wake turbulence.4. Produce a model to correct for the global blockage that an entire wind farm represents to the oncoming wind.5. Finally, develop a low-cost, physics-based wind farm optimisation tool and disseminate it to the UK's wind-energy sector. The model will take as inputs the details of the turbines to be erected, the atmospheric conditions at the specified site and the agreed strike price/MWh to be paid for the generated power. The output will be the optimal number and layout of wind turbines for an efficient offshore wind farm. We have attracted three partners from across the wind-energy sector who will play a vital role in ensuring that the output of this research is disseminated to the key stakeholders in the UK in a form that can be implemented by the industry straight away.

风能目前占英国电力的18%，但在2050年实现去碳化经济的努力中，这一比例必须在未来十年大幅增加。英国政府承诺在2030年之前每年增加海上风电装机容量1-2千兆瓦，这反映出英国拥有欧洲一些最适合海上风能的地点。随着英国对风能的依赖程度越来越高，提高风电场的效率和可靠性变得越来越重要。由于位于上游机器尾迹的风力涡轮机产生的功率比上游机器产生的功率更少，承受的疲劳负荷更高，因此有机会通过提高我们预测风力涡轮机产生的尾迹的能力来实现这一目标，从而在了解当时的风况的情况下设计出一个布局最优的风电场。由于过度依赖过时的经验主义，我们优化风力发电场的能力目前受到了阻碍。这项提议试图通过开发基于物理的建模工具来纠正这一点，以更好地描述单个风力涡轮机尾迹以及风电场内相互作用的尾迹之间的相互作用。海上风力发电场特别容易优化，因为与陆上场地相比，当时的风力条件很稳定。风力涡轮机的最佳间距取决于几个因素。这些都是希望从给定的地点生产尽可能多的电力，同时将维护成本降至最低，以应对涡轮机位于上游机器高度不稳定、颠簸的尾流中造成的疲劳损坏。这需要对风力涡轮机尾迹扩散的可靠预测，以及一种根据预测的流入条件估计风力涡轮机部件疲劳寿命的方法。此外，还存在预测全球阻塞的问题，在该阻塞中，风电场作为一个整体具有将风转移到风电场上方/周围的效果，这意味着流入该风电场的真实风速与当时的风速不同。具体来说，我们将：1.进行创新性的实验，以更好地了解湍流尾迹传播的流动物理基础。这将涉及探索在多个长度尺度上同时引入的相干之间在近尾迹中的相互作用，例如，塔、机舱和叶尖漩涡。此外，我们还将探索由于卷吸现象而产生的尾迹扩散背后的物理原理，卷吸现象是质量/能量从背景转移到尾迹的过程。我们将特别关注大气湍流和尾迹湍流对卷吸的影响。利用这种新的物理理解，并将其转化为一个基于物理的模型，用于单个风力涡轮机尾迹的传播。设计一种方法来准确预测易受攻击的风力涡轮机部件(例如齿轮箱/后缘粘接等)的疲劳寿命。响应大气/尾迹湍流引起的脉动入流。制作一个模型，以修正整个风电场对迎面而来的风所代表的全球阻塞。最后，开发一种低成本、基于物理的风电场优化工具，并将其推广到英国的风能行业。该模型将把将安装的涡轮机的细节、指定地点的大气条件以及为发电支付的商定执行价格/兆瓦时作为输入。其输出将是高效海上风电场所需的最佳风力涡轮机数量和布局。我们已经吸引了来自风能行业的三个合作伙伴，他们将在确保这项研究的结果以行业可以立即实施的形式传播给英国的关键利益相关者方面发挥至关重要的作用。