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.
