CAREER: Physics-Reinforced Data-Driven Prognostics and Co-Design for Marine Hydrokinetic Energy Systems

Monday, Feb 07, 2022
Yufei Tang

This Faculty Early Career Development (CAREER) project will fundamentally advance knowledge related to the monitoring and design of marine and hydrokinetic (MHK) energy systems, including marine current turbines and wave energy converters. MHK systems could contribute significantly to a diversified energy economy, improving the nation’s energy security and reducing reliance on fossil fuels. However, these systems generate power from puissant resources, such as strong water currents and/or large waves, which impose physical stresses on the equipment that are several times greater than wind turbines of similar power ratings. These constraints lead to stringent design requirements that increase capital costs. Further, operation and maintenance costs are high because access to equipment is limited due to their offshore geographical location and harsh corrosive environments. This research project will provide the theoretical and computational foundation to enhance MHK systems’ maintainability, survivability, and efficiency. The long-term goal of this project is to transform the conventional MHK turbine design process from a sequential approach, where subsystems are designed individually and strong coupling among them is neglected, generally leading to a suboptimal design, to a novel co-design framework that simultaneously accounts for control, reliability and operational expenditure of the overall MHK system with coupled subsystems. This simultaneous co-design at the earliest stage allows for mutually beneficial subsystems and could significantly improve the overall system performance. This project will thus improve energy systems and accelerate progress in the blue economy. Results will be disseminated in collaboration with the National Renewable Energy Lab and industry partners, as well as through open-source tools, accelerating technology transfer. Outcomes will be integrated into new research-intensive curricula and a new energy resiliency certificate, and opportunities will be provided to students from groups underrepresented in STEM to participate in marine renewable energy research.

This research project aims to develop efficient and robust prognostics (prediction of the remaining useful life) and diagnostics (fault detection and identification) tools of MHK turbines, for the goal of establishing a unified design framework that accounts for control, reliability, and operational expenditure of MHK systems. The project will explore a spectrum of tools from domain mechanistic models to deep learning. The research activities will integrate domain-specific physics knowledge and multi-source data in a synergistic manner. Specifically, the project will address the following three research challenges. (1) The data scarcity challenge: there are not enough data to train an effective prognostics/diagnostics model for MHK because the industry is new. This project will develop a novel physics-reinforced knowledge transfer learning approach for designing efficient models that uses wind big data as the source domain, constrained by the physics in MHK as the target domain. (2) The data quality and concept drift challenge: system dynamics may change over time and sensor data are subject to failures because MHK devices are to be deployed in harsh, remote areas for long-term operation. This project will develop a novel graph and reinforcement learning approach for designing robust models using both sensor network structure information and stream pattern of multi-sensor time series. (3) The heterogeneous, multi-directional couplings and co-optimization challenge: turbine geometry, control, reliability, and maintenance strategies should be designed simultaneously to optimize MHK turbine performance. This project will build responsive surface models, that represent the relationships between design parameters and performance index, based on both experimental data and dynamical simulations. White-box co-optimization tools based on deep neural decision trees will be developed to optimize the turbine design parameters. Taken together, results from this research will establish a solid foundation for robust predictive monitoring and co-design of complex large-scale dynamic systems, such as onshore and floating offshore wind farms, connected vehicles, and intelligent structures.

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.

Abstract retrieved from:  NSF

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