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

为了让美国在2030年前实现30吉瓦的海上风力发电目标，未来几年需要安装大约2000台海上风力涡轮机(OWTs)。目前只有7艘在美国水域作业。相比之下，近6000个基金会在欧洲水域运营。由于OWTs的规模、费用和对减缓气候变化的重要性，有必要将其视为民用基础设施。这样的基础设施必须能够持续使用50年甚至100年以上。然而，将OWTs作为基础设施的概念并没有跟上它们的快速增长。大多数OWTs的设计寿命通常为25至35年。几十年后，该行业将面临一个相应的决策挑战，即是否退役、重建或翻新这些资产。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，该团队开发了一个联合建模框架，用于OWT安全、运维、寿命延长和设计的决策。国际合作至关重要，因为海上风电场的开发商、设计者和运营者几乎完全来自欧洲。利用来自国际合作者的欧洲经验，研究人员使用直接从OWTs、为OWTs服务的工人和使其得以发展的决策者那里直接收集的定量和定性数据。他们的目标是找到解决方案，以提高海上风力涡轮机的弹性，特别是在面临更频繁的极端天气事件时。通过延长OWT的使用寿命，该项目为高效发展美国清洁能源基础设施铺平了道路。该项目还为1名博士后助理以及研究生和本科生提供支持和培训，这些学生主要来自科学和工程领域代表性不足的群体。更具体地说，该团队为具有政策和安全意识的数字双胞胎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-安全联合建模范例，并在基于代理的模型的帮助下集成。数字双胞胎是实际OWT(或OWT系统)的计算模型，基于测量数据进行维护和更新。拟议的框架是在罗德岛州水域的区块岛风力发电场、美国联邦水域的弗吉尼亚海岸离岸风力试点项目和英国的莱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、劳动力和安全方面相结合。可再生能源和相关结构要素的多领域、多尺度性质由基于物理学的贝叶斯同化框架模拟。它与数据驱动的机器学习和迁移学习相辅相成。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共同知识生产。该项目包含完整的教育部分，包括K-12外展、专业培训和跨学科研究环境中研究生的发展，以支持海上风电行业未来所需的劳动力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.