Year 1 timetable

Title: Physics-informed machine learning for wind energy applications

Abstract: Wake interactions in wind farms significantly influence power production and structural loads on wind turbines. Current numerical tools for wake prediction mainly fall into two categories: computational fluid dynamics (CFD) models, which are accurate but computationally expensive, and analytical wake models, which are fast but lack accuracy. To bridge the gap between accuracy and computational efficiency, the first part of this work focuses on developing machine learning (ML)-based wake models capable of real-time evaluation, capturing high-fidelity flow features, generalizing well to unseen flow scenarios, and scaling effectively to large wind farms. The second part of the work introduces a data-driven, physics-informed digital twin of the wind farm flow system. By integrating LIDAR and turbine sensor data with physics-based constraints derived from the Navier–Stokes equations and actuator modeling of turbine rotors, the digital twin enables accurate in situ prediction of spatiotemporal wind farm flow fields.

Bio: Dr. Jincheng Zhang is an Assistant Professor at the School of Engineering, University of Warwick. He obtained his B.S. and M.S. degrees in Mechanical Engineering from Tsinghua University in 2015 and 2018, respectively, and his PhD from the University of Warwick in 2021. His research focuses on data-driven and physics-informed deep learning, wave and wind energy, CFD simulations, AI for fluids, and uncertainty quantification.