FENGBO-WIND - Farming the ENvironment into the Grid: Big data in Offshore Wind

FENGBO-WIND - 将环境融入电网：海上风电大数据

• 批准号: EP/R007470/1
• 负责人: John Graham
• 金额: $ 103.52万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR007470%2F1
• 关键词: FENGBO; WIND; Farming; ENvironment; into

The proposed project will develop an integrated computational simulation approach capable of handling the complex interactions between the local atmosphere, the coastal ocean and sedimentary environment, farm aerodynamics, turbine response and grid integration in offshore wind farms. This will target a substantial reduction in the cost of energy in offshore wind by exploiting: high-fidelity optimization of array design and operation, tailored to a specific site and able to deal with realistic marine atmospheric boundary layer conditions, in particular the very slow dissipation of rotor wakes; combined with big-data analysis of very-large-scale simulations of the whole system under extreme conditions, to minimize integrity risks without overly conservative safety factors. Both situations will be investigated within the context of the development of offshore farms off the Chinese coast, which brings particular challenges regarding coastal characteristics (e.g. high sediment concentrations) and extreme events (in particular typhoons). To achieve this we propose a multiscale approach to wind farm design and network integration that considers, first, a more accurate characterisation of extreme events (and active mitigation strategies) in the analysis through highly-resolved computer simulation; second, new optimization techniques for the design and operation of wind farms that allow for sustained power extraction using relevant knowledge of both the marine atmosphere and individual turbine (aeroservoelastic) dynamics; and third, robust grid design and operation strategies that accommodate wind resource variability and maximise the sustainability of energy generation. FENGBO-WIND will carry out the most ambitious computer simulations to date on farm dynamics and farm/environment interaction, to build physics-based predictive capabilities on farm output and investigate long-term interactions between farms and their local environment.An interdisciplinary consortium of experts, including Earth/environmental scientists, civil and electrical engineers, and fluid dynamicists, have been assembled to tackle this challenging computational problem. The team will have access to (1) the world's largest supercomputer (Sunway TaihuLight) to carry out full system simulations of energy output and farm state for specific environmental scenarios, (2) operational data from existing wind farms off the Chinese coast as well as conditions at a target site through a partnership with a local grid company, and (3) performance data for a state-of-the-art wind turbine design from the leading Chinese manufacturer. The results will be benchmarked against state-of-the-art industrial design tools and protocols for grid integration for offshore wind farms.

拟议的项目将开发一种综合计算模拟方法，能够处理当地气氛，沿海海洋和沉积环境，农场空气动力学，涡轮机响应和网格整合之间的复杂相互作用。这将通过利用近海风的能源成本大大降低：对阵列设计和操作的高保真优化，该阵列设计和操作是针对特定地点量身定制的，并且能够处理逼真的海洋大气边界层条件，特别是转子醒来的耗散非常缓慢；结合大数据在极端条件下对整个系统的非常大规模的模拟分析，以最大程度地降低诚信风险而没有过度保守的安全因素。两种情况都将在中国沿海的离岸农场的发展中进行调查，这带来了有关沿海特征（例如高沉积物浓度）和极端事件（尤其是台风）的特殊挑战。为了实现这一目标，我们提出了一种多尺度方法，用于风场设计和网络集成，首先，通过高分辨率的计算机模拟，在分析中考虑了极端事件（和主动缓解策略）的更准确表征；其次，针对风电场设计和运行的新优化技术，可以使用有关海洋大气和单个涡轮机（Aeroservocolastic）动力学的相关知识来持续提取功率。第三，强大的网格设计和操作策略可容纳风能变化并最大程度地发挥能源的可持续性。爆发范围将在农场动态和农场/环境互动上进行最雄心勃勃的计算机模拟，以建立基于物理的预测能力，并研究农场与其当地环境之间的长期互动。跨学科的专家联盟，包括地球/环境科学家，民用和电气工程师和电气工程师，以及流动的动态主义者，以及对这个挑战性的问题。该团队将可以使用（1）世界上最大的超级计算机（Sunway Taihulight），以对能源输出和农场状态进行完整的系统模拟，以解决特定环境方案，（2）通过与当地的Grid Company的合作伙伴关系，以及（3）绩效数据，从中国海岸的现有风电场进行操作，以及针对领先的Winder the Art Pradition Pradition Pradition Pradition the Pradition Partive frade traber with the Castric witchine with trabine witchine witchine with the the Artister of Pradition temally的情况。结果将根据最先进的工业设计工具和协议进行基准测试，以用于海上风电场的电网集成。

项目成果

Assessing erosion and flood risk in the coastal zone through the application of multilevel Monte Carlo methods

通过应用多级蒙特卡罗方法评估沿海地区的侵蚀和洪水风险

• DOI: 10.1016/j.coastaleng.2022.104118
• 发表时间: 2022
• 期刊: Coastal Engineering
• 影响因子: 4.4
• 作者: Clare M

Wind Energy Handbook

• DOI: 10.5860/choice.49-2091
• 发表时间: 2001-11
• 作者: T. Burton;D. Sharpe;E. Bossanyi;N. Jenkins

WITHDRAWN: Assessing erosion and flood risk in the coastal zone through the application of multilevel Monte Carlo methods

撤回：通过应用多级蒙特卡罗方法评估沿海地区的侵蚀和洪水风险

• DOI: 10.1016/j.coastaleng.2021.103850
• 发表时间: 2021
• 期刊: Coastal Engineering
• 影响因子: 4.4
• 作者: Clare M

Hydro-morphodynamics 2D modelling using a discontinuous Galerkin discretisation

使用不连续伽辽金离散化进行流体形态动力学二维建模

• DOI: 10.1016/j.cageo.2020.104658
• 发表时间: 2021
• 期刊: Computers & Geosciences
• 影响因子: 4.4
• 作者: Clare M

Xcompact3D: An open-source framework for solving turbulence problems on a Cartesian mesh

• DOI: 10.1016/j.softx.2020.100550
• 发表时间: 2020-07
• 期刊: SoftwareX
• 影响因子: 3.4
• 作者: P. Bartholomew;G. Deskos;R. Frantz;F. Schuch;E. Lamballais;S. Laizet