Probabilistic Performance Assessment and Control Co-Design of Wave Farms

本项目的目标是了解如何将工程系统的设计与控制相结合，以实现复杂大系统的系统最优性能。传统的顺序/迭代系统设计及其控制是昂贵的，并且忽略了两者之间存在的设计耦合；这可能导致次优设计。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颖的、健壮的联合设计框架和解决方案方法，通过适当考虑设计耦合和不确定性，使系统及其控制能够进行集成设计。这项研究将导致更稳健和最优的工程系统。虽然这项研究将集中在大规模波浪发电场的工厂/布局和控制协同设计中，其中包括许多具有复杂动力学特性的相互作用的波能转换器(WEC)，但该框架和方法可移植和推广到波浪发电场以外的工程系统的设计中，如风电场中的陆上和浮动海上风力发电机组、车辆设计、智能天线和智能结构。研究成果将有助于改善经济和能源安全，并确保在清洁能源和系统设计方面继续领先和创新，从而推动设计科学，促进国家繁荣和福祉。这项研究的目的是调查波浪养殖场中大规模WEC阵列的概率性能，以及控制协同设计对其性能(例如，功率输出、动态运动和加载以及生命周期性能)的影响，考虑阵列内部的复杂相互作用、WEC的动力学和控制以及波浪条件中的不确定性。一个稳健的对象和控制联合设计问题将被用来优化WEC阵列的概率性能，其中将使用基于仿真的方法来显式地考虑各种不确定性。提出了一种结合单保真和多保真贝叶斯优化的新颖高效的嵌套求解策略。为了有效地模拟大规模阵列，还将开发一种基于多体展开和代理模型的新方法。这项研究将促进我们对大规模波浪场概率性能和设计的科学认识，并通过建立稳健的控制协同设计公式和有效的求解策略来推进现有的控制设计范式。所开发的方法是通用的，可以应用于多个应用领域，其中控制协同设计和不确定条件下的系统设计是关键方面。本项目中的新的代理建模方法也将促进大规模工程系统性能预测的进步。这项研究将产生广泛的社会影响，帮助提高可再生能源的经济可行性和竞争力，降低能源成本，并确保在清洁能源和系统设计方面继续保持国家领先地位和创新。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，以及面向科学教师的外联活动，以改善STEM教育者的发展，并帮助激励和发展一支具有全球竞争力的STEM工作队伍。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。

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.