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

面对欧盟绿色协议设定的气候神经网络目标，模块化能源岛建议利用深海有吸引力的风能。作为一个因素，一个突出的结构挑战出现了，即在恶劣的海洋环境，突出的循环载荷和大量的焊接连接的组合下，风塔的腐蚀疲劳恶化。因此，TwinsTower行动旨在通过整合物理模型、检查结果和监测数据，为更好地理解模块化能源岛风塔的腐蚀疲劳做出新的和实际的贡献。经验研究（ER）将：(1)建立模块化能源岛风塔腐蚀疲劳综合预测模型；（ii）构建基于数字孪生的模块化能源岛风塔预测方法，并将监测和检查结果集成。在伯明翰大学主席Charalampos Baniotopoulos教授的监督下，这一行动将使他能够通过发展他在风能研究、数据科学、知识传播和利用、网络、监督、教学、研究管理和领导方面的技能，使他的能力多样化。这一行动也将极大地有利于急诊室的跨部门和跨学科专业知识，并加强考虑借调到Ruhr-Universität波鸿的国际网络。由于该行动整合了ER在腐蚀疲劳预测、退化概率建模和工程实践方面的经验，以及主机在塔设计和细节、深度学习和SHM数据利用方面的专业知识，因此保证了知识的双向传递。总而言之，双子塔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士做出贡献。

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.