Dr. Ryan King is a senior scientist with NREL's Computational Science Center; he joined NREL as a graduate student in 2012. His research focuses on optimization and machine learning applied to complex energy systems and turbulent flows. While pursuing his Ph.D. at the University of Colorado, Dr. King used advanced adjoint optimization techniques to improve wind plant design and controls and developed a new data-driven machine learning closure for turbulence modeling. As a postdoc at NREL, he studied importance sampling for stochastic economic dispatch problems that can arise due to intermittent wind generation. He also developed a data-assimilation framework to improve wind plant flow model performance in different atmospheric stability conditions. Prior to graduate school, Dr. King worked as an engineer at RES Americas where he was involved in the design and construction of over 750 MW of operational wind energy.
Reduced order modeling
Ph.D., Mechanical Engineering, University of Colorado, Boulder
B.S., Mechanical Engineering, Massachusetts Institute of Technology