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 Our ability to optimise wind farms is currently hampered by an over-reliance on out-of-date empiricism.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.

目前，风能占英国电力的 18%，但为了到 2050 年实现脱碳经济，这一比例必须在未来十年大幅增加。英国政府承诺到2030年每年增加海上风电容量1-2吉瓦，这反映出该国拥有欧洲一些最佳的海上风电地点。随着英国越来越依赖风能，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流中的风力涡轮机比上游机器产生的功率更少并且承受更高的疲劳载荷，因此通过提高我们预测风力涡轮机产生的尾流的能力，从而设计最佳布局的风力涡轮机，可以实现这一目标。农场了解盛行风况。目前，我们优化风电场的能力因过度依赖过时的经验主义而受到阻碍。该提案旨在通过开发基于物理的建模工具来纠正这一问题，以更好地描述单个风力涡轮机尾流以及风电场内相互作用的尾流之间的相互作用。与陆上风电场相比，由于盛行风况的稳定性，海上风电场特别适合优化。风力涡轮机的最佳间距取决于几个因素。人们希望在给定地点产生尽可能多的电力，同时最大限度地减少维护成本，以应对上游机器高度不稳定、湍流尾流中的涡轮机造成的疲劳损坏。这需要对风力涡轮机尾流的传播进行可靠的预测，并需要一种方法来估计风力涡轮机部件的疲劳寿命，以响应其预测的流入条件。此外，还存在预测全球阻塞的问题，其中风电场作为一个整体具有使风电场上方/周围的风转向的效果，这意味着风电场的真实流入风速与盛行风速不同。风。具体来说，我们将： 1．进行创新实验，以便更好地了解支持湍流尾流传播的流动物理学。这将涉及探索近尾流中由塔架、机舱和叶尖涡流等同时引入的多个长度尺度的相干性之间的相互作用。此外，我们将探索由于夹带现象而产生的尾流扩散背后的物理原理，夹带现象是质量/能量从背景转移到尾流的过程。我们将特别关注大气、尾流、湍流对夹带的影响。2．采用这种新的物理理解，并将其转化为基于物理的模型，用于单个风力涡轮机尾流的传播。3。设计一种方法，准确预测易损风力涡轮机部件（例如齿轮箱/后缘粘结等）的疲劳寿命，以响应大气/尾流湍流引起的波动流入。4。建立一个模型来纠正整个风电场对迎面而来的风的整体阻碍。5。最后，开发一种低成本、基于物理的风电场优化工具，并将其推广到英国风能行业。该模型将以待安装涡轮机的详细信息、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。输出将是高效海上风电场的最佳风力涡轮机数量和布局。我们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成果以行业可以立即实施的形式传播给英国的主要利益相关者方面发挥至关重要的作用。