PIRE: Multi-Domain, Multi-Scale, Policy-Aware Digital Twin for Offshore Wind Energy Infrastructure

为了使美国在2030年前实现30吉瓦的海上风电目标，未来几年需要安装大约2000台海上风力涡轮机（owt）。目前只有7艘在美国水域作业。相比之下，欧洲海域有近6000个基金会在运作。从其规模、费用和对减缓气候变化的重要性来看，有必要将其视为民用基础设施。这些基础设施必须能够使用50年甚至100年以上。然而，将owt视为基础设施的概念并没有跟上它们的快速增长。大多数wt通常设计为25至35年的使用寿命。在未来的几十年里，油气行业将面临一个重大的决策挑战，即是退役、重建还是改造这些资产。这一挑战并不局限于美国。考虑到海上风能的全球扩张，它具有全球影响。在这里，团队开发了一个联合建模框架，用于OWT安全、运行和维护、寿命延长和设计方面的决策。国际合作至关重要，因为海上风力发电场的开发商、设计师和运营商几乎全部来自欧洲。利用来自他们的国际合作者的欧洲经验，研究人员使用直接从owt、为他们服务的工人和使他们发展的决策者那里收集的定量和定性数据。他们的目标是找到提高海上风力涡轮机弹性的解决方案，特别是在面临更频繁的极端天气事件时。通过提高OWT的使用寿命，该项目为美国清洁能源基础设施的高效发展铺平了道路。该项目还为1名博士后、研究生和本科生提供支持和培训，特别是来自科学和工程领域代表性不足的群体。更具体地说，该团队为具有策略和安全意识的数字孪生开发了一个可扩展和可定制的联合建模框架。它们使用物理-数据-策略-安全协同建模范式，并在基于Agent的模型的帮助下集成。数字孪生是基于测量数据维护和更新的实际OWT（或OWT系统）的计算模型。拟议的框架是在罗德岛州水域的布洛克岛风电场、美国联邦水域的弗吉尼亚沿海海上风电试点项目和英国的利文茅斯示范涡轮机的背景下制定和研究的。它可以对海上风电场进行短期、中期和长期的建模、学习和评估。它使用测量数据，并与政策、劳动力和安全方面集成。可再生能源和相关结构元素的多领域、多尺度特性由基于物理的贝叶斯同化框架建模。辅以数据驱动的机器学习和迁移学习。该项目包括国家和国际利益攸关方参与计划，以促进多样化和包容性的知识共同生产。该项目具有完整的教育组成部分，包括K-12外展，专业培训和跨学科研究环境中的研究生发展，以支持海上风电行业未来所需的劳动力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

For the US to achieve the offshore wind goals of 30 Gigawatts by 2030, approximately 2000 offshore wind turbines (OWTs) need to be installed in the coming years. Only 7 are currently operating in US waters. For comparison, close to 6000 foundations are operating in European waters. The size, expense, and importance of OWTs to mitigating climate change necessitates to consider them as civil infrastructures. Such infrastructures must be built to last for over 50 or even 100 years. However, a conception of OWTs as infrastructure has not caught up with their rapid growth. Most OWTs are typically designed for a 25-to-35-year service life. In a few decades, the industry will face a consequential decision-making challenge as to whether to decommission, rebuild, or retrofit these assets. This challenge is not limited to the US. It has global implications considering the global expansion of offshore wind energy. Here, the team develops a joint modeling framework for decision making about OWT safety, operation and maintenance, life extension and design. International collaboration is essential because the developers, designers, and operators for offshore wind energy farms are almost entirely from Europe. Leveraging the European experience from their international collaborators, the researchers use both quantitative and qualitative data collected directly from OWTs, the workers who service them, and the decision makers who enable their development. Their goal is to find solutions to improve the resilience of offshore wind turbines, notably while confronted to more frequent extreme weather events. By improving OWT service life, this project paves the way to efficiently develop the US clean-energy infrastructures. The project also provides support and training to 1 postdoctoral associate, and graduate and undergraduate students notably from underrepresented groups in science and engineering. More specifically, the team develops an extensible and customizable joint modeling framework for policy and safety aware digital twins. They use a physics-data-policy-safety co-modeling paradigm, integrated with the help of Agent Based Models. A digital twin is a computational model of an actual OWT (or systems of OWTs) that is maintained and updated based on measured data. The proposed framework is formulated and studied within the context of the Block Island Wind Farm in Rhode Island state waters, the Coastal Virginia Offshore Wind Pilot Project, in US federal waters, and the Levenmouth Demonstration Turbine in the United Kingdom. It enables short, medium, and long-term modeling, learning, and assessment of the offshore wind farms. It uses measured data and integrate with policy, labor, and safety aspects. The multi-domain, multi-scale nature of the renewables and related structural elements is modeled by a physics-based Bayesian Assimilation Framework. It is complemented by data-driven machine learning and transfer learning. The project includes national and international stakeholder engagement programs to facilitate a diverse and inclusive co-production of knowledge. The project has integral educational components including K-12 outreach, professional trainings, and development of graduate students within a transdisciplinary research environment to support the future workforce needed in the offshore wind industry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.