Webinar: High Fidelity Modelling Capabilities in NREL for Wind Energy

Senu Sirnivas is responsible for leading and executing research programs to address emerging issues in the development of offshore wind energy technology. He advises, facilitates, and executes laboratory initiatives in offshore wind and marine hydrokinetics working closely with the U.S. Department of Energy, industry, and university research partners. Prior to joining NREL, he worked in the offshore oil and gas industry for 20 years. Starting at Deep Oil Technology, the original Spar technology company, he has transitioned through Aker Maritime, Coflexip Stena, and Technip. Prior to leaving Technip, he was the head of the Floater Product Line's software development group. In his last position, at Horton Wison Deepwater, he led the efforts on offshore floating wind business development.

Mike Sprague is a Chief Computational Scientist in the National Wind Technology Center at the National Renewable Energy Laboratory (NREL).  He is leading several projects in high-fidelity modeling and high-performance computing for wind energy. He was program chair for the 2015 U.S. Department of Energy "Turbulent Flow Simulation at the Exascale: Opportunities and Challenges Workshop." Before coming to NREL in 2010, Dr. Sprague spent five years as assistant professor and founding faculty in applied mathematics at the University of California, Merced, where he was the lead in developing a new graduate program in applied mathematics. He graduated with a PhD in Mechanical Engineering from the University of Colorado at Boulder and a BS from the University of Wisconsin-Madison.

Ganesh Vijayakumar is a researcher at the National Wind Technology Center. Ganesh completed his Ph.D. in Mechanical Engineering from Pennsylvania State University on quantifying the aerodynamic loads on wind turbines due to atmospheric turbulence using computational fluid dynamics. He is one of the main developers of the ExaWind modeling and simulation environment for wind energy at NREL focusing on fluid-structure interaction and wind turbine aerodynamics. Ganesh’s recent work is focused on creating a pipeline from high-fidelity simulations to design using machine learning. He is the principal investigator of an ARPA-E project on inverse-design of wind turbines using invertible neural networks. He also developed new machine learning models for unsteady aerodynamics and generator design.