Probabilistic Performance Assessment and Control Co-Design of Wave Farms

本项目的目标是了解集成工程系统的设计及其控制，以实现复杂的大规模动态系统的系统优化性能的原则。传统的顺序/迭代设计的系统，然后其控制是昂贵的，忽略了设计之间存在的耦合，这可能会导致次优设计。此外，需要考虑环境中的各种不确定性，以建立稳健的设计。这项研究将开发一种新的，强大的协同设计框架和解决方案的方法，这将使系统的集成设计和控制，适当考虑设计耦合和不确定性。该研究将导致更强大和最佳的工程系统。虽然这项研究将侧重于应用于工厂/布局和控制协同设计的大型波浪农场，其中包括许多相互作用的波能转换器（WEC）与复杂的动态，框架和方法是可转移和推广的工程系统的设计超出波浪农场，如陆上和浮动海上风力涡轮机在风力发电场，车辆设计，智能天线和智能结构。研究成果将通过帮助改善经济和能源安全以及确保清洁能源和系统设计的持续领导和创新，促进设计科学和国家繁荣和福利。本科生、研究生和科学教师将接受培训，所有新方法和算法将开源共享，以接触和影响更广泛的受众，包括工业界。本研究旨在调查波浪农场中大规模WEC阵列的概率性能以及控制协同设计对其性能的影响（例如，功率输出、动态运动和载荷以及寿命周期性能），同时考虑到阵列内的复杂相互作用、WEC的动力学和控制以及波浪条件的不确定性。一个强大的工厂和控制协同设计问题将制定优化WEC阵列的概率性能，其中基于仿真的方法将被用来明确地考虑各种不确定性。一个新的和有效的嵌套解决方案的战略集成单保真度和多保真度贝叶斯优化将开发。为了有效地模拟大规模阵列，一种新的方法的基础上多体膨胀和代理模型也将被开发。该研究将推进我们的概率性能和设计的大型波浪农场的科学知识，并通过建立强大的控制协同设计公式和有效的解决方案策略，推进现有的控制设计范例。待开发的方法是通用的，可以应用到多个应用领域的控制协同设计和系统设计下的不确定性是关键的方面。该项目中新颖的替代建模方法也将导致大规模工程系统性能预测的进步。该研究将有助于提高可再生能源的经济可行性和竞争力，降低能源成本，确保国家在清洁能源和系统设计方面的持续领导和创新，从而产生广泛的社会影响。与国家可再生能源实验室合作以及通过开源工具传播成果将加快技术转让。综合教育计划包括培训研究生和本科生研究人员，创建新的工程课程，以及针对科学教师的外展活动，以提高STEM教育者的发展，并帮助激励和培养具有全球竞争力的STEM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价值和更广泛的影响审查标准进行评估，被认为值得支持。

The objective of this project is to understand the principles of integrating the design of an engineering system and its control to achieve system-optimal performance for complex large-scale dynamic systems. Traditional sequential/iterative design of a system and then its control is costly and neglects the design coupling that exists between the two; this may lead to sub-optimal designs. Also, various uncertainties in the environment need to be accounted for to establish robust designs. This research will develop a novel, robust co-design framework and solution methodologies that will enable the integrated design of systems and their control by properly accounting for design coupling and uncertainties. The research will lead to more robust and optimal engineering systems. While this research will focus on application to plant/layout and control co-design of large-scale wave farms, which include many interacting wave energy converters (WECs) with complex dynamics, the framework and methodologies are transferable and generalizable to the design of engineering systems beyond wave farms, such as onshore and floating offshore wind turbines in wind farms, vehicle design, smart antennas, and intelligent structures. Research outcomes will advance design science and advance national prosperity and welfare by helping to improve both economic and energy security and by ensuring continued leadership and innovation in clean energy and system design. Undergraduate students, graduate students, and science teachers will be trained, and all new methods and algorithms will be shared open-source to reach and impact a broader audience, including industry.This research aims to investigate the probabilistic performance of large-scale arrays of WECs in wave farms and the impacts of control co-design on their performance (e.g., power output, dynamic motion and loading, and life-cycle performance), taking into account the complex interactions within the array, the dynamics and control of WECs, as well as uncertainties in the wave conditions. A robust plant and control co-design problem will be formulated to optimize the probabilistic performance of WEC arrays, where a simulation-based approach will be used to explicitly account for various uncertainties. A novel and efficient nested solution strategy integrating both single-fidelity and multi-fidelity Bayesian optimization will be developed. To efficiently model large-scale arrays, a novel method based on many-body expansion and surrogate models will also be developed. The research will advance our scientific knowledge of probabilistic performance and design of large-scale wave farms, and advance existing control design paradigms by establishing robust control co-design formulations and effective solution strategies. The methodologies to be developed are general and can be applied to multiple application domains where control co-design and system design under uncertainty are critical aspects. The novel surrogate modeling approach in this project will also lead to advances in performance prediction of large-scale engineering systems. The research will have broad societal impact by helping to improve the economic feasibility and competitiveness of renewable energy, reducing the cost of energy, and ensuring continued national leadership and innovation in clean energy and system design. Dissemination of the results in collaboration with the National Renewable Energy Lab, as well as through open-source tools, will accelerate technology transfer. The integrated education plan involves training of graduate and undergraduate researchers, creation of new engineering courses, as well as outreach activities to science teachers to improve STEM educator development and to help inspire and develop a globally competitive STEM workforce.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.