Digital Twins-based integrated corrosion fatigue prognosis of wind turbines Towers in modular energy islands

基于数字孪生的模块化能源岛风力涡轮机塔架腐蚀疲劳综合预测

• 批准号: EP/X022765/1
• 负责人: Charalampos Baniotopoulos
• 金额: $ 26万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX022765%2F1
• 关键词: Digital; Twins; based; integrated; corrosion

Facing the goal of climate neural set by the EU Green Deal, the modular energy island is suggested to utilise the attractive windpower at the deep sea. As a matter of factor, a prominent structural challenge arises, i.e., the corrosion fatigue deterioration of windtowers under the combination of the harsh marine environment, prominent cyclic loads, and a copious number of weldedconnections. Thus, the TwinsTower action aims to develop new and practical contributions towards a better understanding of thecorrosion fatigue of wind towers in modular energy islands, with both the physical model, inspection result and monitoring dataintegrated. The experienced research (ER) will: (i) establish an integrated corrosion fatigue prediction model for wind towers in themodular energy island; (ii) construct a digital twins-based prognosis approach for wind towers in modular energy islands, with themonitoring and inspection result integrated.Implemented at the University of Birmingham, as supervised by the Chair Prof Charalampos Baniotopoulos, this action will enable theER to diversify his competence by developing his skills in wind energy research, data science, knowledge dissemination andexploitation, networking, supervision, teaching, research management and leadership. This action will also strongly benefit the ER'sinter-sectoral and interdisciplinary expertise and strengthen the international network considering a secondment at the Ruhr-Universität Bochum.A two-way transfer of knowledge is guaranteed since the action integrates the ER's experience in corrosion fatigue prediction,probabilistic modelling of deterioration, and engineering practises as well as the hosts' expertise in tower design and detailing, deeplearning, and SHM data exploitation. To sum, the TwinsTower action could contribute to the EU's knowledge-based society,policymakers and professionals by offering invaluable knowledge and a practical approach supporting the goal of climate neural.

针对欧盟绿色协议提出的气候神经目标，建议采用模块化能源岛，以利用具有吸引力的深海风能。作为一个因素，出现了一个突出的结构性挑战，即，风塔在恶劣的海洋环境、显著的循环载荷和大量的焊接接头的共同作用下，其腐蚀疲劳性能恶化。因此，TwinsTower行动旨在通过整合物理模型、检查结果和监测数据，为更好地了解模块化能源岛中风塔的腐蚀疲劳做出新的实际贡献。经验研究（ER）将：（i）建立模块化能源岛风塔的完整腐蚀疲劳预测模型;（ii）为模块化能源岛中的风塔构建基于数字孪生的预测方法，将监测和检查结果整合在一起。在伯明翰大学实施，由Chair Prof Charalampos Baniotopoulos监督，这一行动将使ER通过发展他在风能研究、数据科学、知识传播和开发、网络、监督、教学、研究管理和领导方面的技能来实现能力的多样化。这一行动也将大大有利于ER的跨部门和跨学科的专业知识，并加强国际网络考虑借调到鲁尔大学波鸿。双向知识转移得到保证，因为行动整合了ER的经验，在腐蚀疲劳预测，概率建模的恶化，和工程经验，以及东道国的专业知识，在塔设计和详细说明，深入学习，和SHM数据开发。总之，TwinsTower行动可以通过提供宝贵的知识和支持气候神经目标的实用方法，为欧盟的知识型社会、政策制定者和专业人员做出贡献。

项目成果

Life-Cycle of Structures and Infrastructure Systems - PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON LIFE-CYCLE CIVIL ENGINEERING (IALCCE 2023), 2-6 JULY, 2023, POLITECNICO DI MILANO, MILAN, ITALY

结构和基础设施系统的生命周期 - 第八届生命周期土木工程国际研讨会 (IALCCE 2023) 论文集，2023 年 7 月 2-6 日，米兰理工大学，意大利米兰

• DOI: 10.1201/9781003323020-167
• 发表时间: 2023
• 作者: Amodio S