Center: IUCRC Phase III UT Dallas: Center for Wind Energy Science, Technology and Research (WindSTAR)

About This Grant

The objective of this project is to establish a multi-university, Phase III I-UCRC (Industry-University Collaborative Research Center) for wind energy research, education, and outreach. The effort is based on a successful ten-year operation led by two university sites (UMass Lowell and the University of Texas at Dallas). Together these two universities have conducted wind energy research, established long-term partnerships within the wind industry, trained undergraduate and graduate students to perform state-of-the-art industry relevant research, engaged in outreach to K-12 students and the international wind energy community. The Center contributes to the nation’s research infrastructure and enhances the intellectual capacity of the energy workforce. An experienced group of scientists, engineers, and practitioners will execute a program of research and education focused on the design, operation, and maintenance of wind energy systems for electricity production. The Center will be aimed at: (a) enhancing national excellence in wind energy research and development that has direct relevance to industry, and (b) developing a cadre of diverse undergraduate and graduate students with world-class training who will support and eventually lead in the analysis, design, manufacture, and successful operation of wind energy systems. This Phase III I-UCRC integrates engineering with fundamental research to support the development of low-cost and high availability wind energy systems. The partners will engage in cooperative research and education in the following key thrust areas: (a) Composites, Blade and Rotor Design & Manufacturing, (b) Structural Health Monitoring and Non-Destructive Inspection, (c) Wind Plant Modeling and Measurements, (d) Control Systems for Wind Turbines and Wind Plants, (e) Energy Storage and Grid Integration, (f) Foundation and Towers, and (g) Topics Beyond the Levelized Cost of Energy Metric. Research led by the UT Dallas site is expected to result in: (1) mechanics-informed machine learning models for prediction of defects in wind blade manufacturing; (2) AI-assisted digital twins to evaluate and predict the condition of critical components; (3) characterization and modelling Farm-to-Farm interactions to inform plant design and control; (4) design for repowering of wind plants to increase capacity factors and guide future designs, (5) fault tolerant wind energy systems; (6) passive devices for performance improvement. The expertise of the site includes high-fidelity simulation of wind power systems; LiDAR measurements and analysis of wind fields for diagnostics and model validation; wind tunnel testing; control system design for wind turbines and wind farms, large rotor design, grid integration and energy storage; applied machine learning for estimation and forecasting. 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.