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.

拟议项目将开发一种综合计算模拟方法，能够处理当地大气、沿海海洋和沉积环境、农场空气动力学、涡轮机响应和海上风电场电网集成之间的复杂相互作用。这将通过以下方式大幅度降低海上风力的能源成本：对阵列设计和操作进行高保真优化，针对具体地点进行调整，并能够处理实际的海洋大气边界层条件，特别是转子尾流消散非常缓慢的情况;结合极端条件下整个系统超大规模模拟的大数据分析，以最大限度地减少完整性风险，而不需要过于保守的安全因素。这两种情况都将在中国沿海开发海上养殖场的背景下进行调查，这带来了沿海特征（例如高沉积物浓度）和极端事件（特别是台风）方面的特殊挑战。为了实现这一目标，我们提出了一个多尺度的方法，风力发电场的设计和网络集成，首先，考虑更准确的描述极端事件通过高分辨率的计算机模拟进行分析（和主动缓解战略）;第二，利用海洋大气和单个涡轮机的相关知识，设计和运行风力发电场的新优化技术，（气动伺服弹性）动力学;第三，稳健的电网设计和运营策略，以适应风力资源的变化，并最大限度地提高能源生产的可持续性。FENGBO-WIND将对农场动态和农场/环境相互作用进行迄今为止最雄心勃勃的计算机模拟，以建立基于物理的农场产量预测能力，并调查农场与当地环境之间的长期相互作用。一个跨学科的专家联盟，包括地球/环境科学家，土木和电气工程师以及流体动力学家，已经聚集在一起来解决这个具有挑战性的计算问题。该团队将有机会使用（1）世界上最大的超级计算机（2）中国沿海现有风电场的运行数据，以及通过与当地电网公司的合作在目标地点的条件，以及（3）来自中国领先制造商的最先进风力涡轮机设计的性能数据。研究结果将以最先进的工业设计工具和海上风电场电网集成协议为基准。

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