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 Our ability to optimise wind farms is currently hampered by an over-reliance on out-of-date empiricism.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

风能目前产生了英国能力的18％，但在驱动到2050年的去碳化经济中，这一比例必须在未来十年内大幅增加。英国政府已承诺每年将海上风力发电能力提高1-2吉瓦，直到2030年，这反映了该国包含欧洲近海风的一些最佳地点。随着英国越来越依赖风能，提高风电场的效率和可靠性至关重要。由于位于上游机器唤醒中的风力涡轮机的功率比上游的风力涡轮机产生的功率更少，而且疲劳载荷更高，因此可以通过提高我们预测风力涡轮机产生的唤醒的能力，从而设计出最佳的风场，从而实现这一目标，鉴于对预先风速的知识。我们优化风电场的能力目前受到过度依赖过时的经验主义的阻碍。该提案旨在通过开发基于物理的建模工具来更好地描述单个风涡轮唤醒以及风电场内的相互作用唤醒之间的相互作用。由于与陆地相比，海上风电场尤其适合优化。这些是从给定站点产生尽可能多的功率的愿望，同时最大程度地减少了维护成本，以应对坐落在高度不稳定，上游机器的高度不稳定，动荡的唤醒中造成的疲劳损害。这需要对风力涡轮机醒来的传播以及一种方法论的信心预测，以估算风力涡轮机成分的疲劳寿命，以响应其预测的流入条件。此外，还有一个问题是预测全球障碍，其中风电场的整个效果是将风转移到风电场周围，这意味着真正的流入风速与盛行的风不相同。具体来说，我们将：1。进行创新的实验，以更好地了解湍流唤醒传播的基础的流动物理。这将涉及探索在塔，纳塞尔和叶片尖端涡流中同时在多个长度尺度下引入的连贯性之间的相互作用中的相互作用。此外，由于夹带的现象，我们将探索产生的尾流的物理物理，这是将质量/能量从背景传递到尾流的过程。特别是，我们将重点关注大气和唤醒对夹带的影响。2。采用这种新的物理理解，并将其转化为基于物理的模型，用于传播单个风涡轮唤醒3。设计一种方法，以对易受伤害的风涡轮组件的疲劳寿命（例如，齿轮盒/尾随边缘键等）进行准确的预测，以响应于大气/尾流湍流引起的波动流入。4。产生一个模型，以纠正整个风电场代表的全球障碍。5。最后，开发低成本的基于物理的风电场优化工具，并将其传播到英国的风能领域。该模型将作为输入，将要架设的涡轮机的详细信息，指定地点的大气条件以及为生成的电源支付的商定的行使价格/MWH。对于有效的海上风电场，输出将是风力涡轮机的最佳数量和布局。我们吸引了来自整个风能领域的三个合作伙伴，他们将在确保这项研究的产出以立即实施的形式中发挥至关重要的作用。