The turbine wake refers to the trail left by the upstream turbines with the decreased wind speed and the increased turbulence intensity. The adverse wake effect correspondingly results in less energy production and triggers earlier fatigue failure of downstream turbines. The talk aims to propose solutions to lifecycle wake mitigation strategies, i.e., layout optimization in the design stage and cooperative yaw control in the operation stage, through advanced machine learning techniques and optimization methods. A machine learning wake model accurately predicts wake characteristics and demonstrates its advantages when applied to the freeform optimization and renovation of wind farms. A novel double-layer machine learning framework involving Bayesian optimization for cooperative wind farm control is established to improve the overall power output in the maintenance stage. Overall, these proposed solutions offer a promising path forward for the robust development of offshore wind farms in the long term.
Professor Xiaowei Deng is an associate professor and coordinator of the Structural Group in the Department of Civil Engineering at the University of Hong Kong. His research interests include numerical modeling by utilization of data-driven artificial intelligent techniques and novel design of lightweight materials and structures for civil, mechanical, and biomedical applications. Professor Deng serves as Past President of the American Society of Civil Engineers (ASCE), Greater China Section, and Co-opted director of the Hong Kong Wind Engineering Society (HKWES). He receives the William F. Ballhaus Prize for an outstanding Ph.D. dissertation from Caltech and two best paper awards from ASME and AIAA, respectively. He also wins the Technological Innovation Award from the China Steel Construction Society and the First Class of Science and Technology Progress Award of Chongqing.
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
