Paper Publications

• [1] 王龙滟.Super-resolution reconstruction framework of wind turbine wake: Design and application
• [2] 王龙滟.Wind turbine dynamic wake flow estimation (DWFE) from sparse data via reduced-order modeling-based machine learning approach
• [3] 王龙滟.A deep learning framework for reconstructing experimental missing flow field of hydrofoil
• [4] 王龙滟.Deep learning enhanced fluid-structure interaction analysis for composite tidal turbine blades
• [5] 王龙滟.A novel generative approach to the parametric design and multi-objective optimization of horizontal axis tidal turbines
• [6] 王龙滟.Effectiveness of wake control optimization for multiple in-line wind turbines by combinatorial machine learning wake model
• [7] 王龙滟.A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements
• [8] 王龙滟.DLFSI: A deep learning static fluid-structure interaction model for hydrodynamic-structural optimization of composite tidal turbine blade
• [9] 王龙滟.Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data
• [10] 王龙滟.A novel generative-predictive data-driven approach for multi-objective optimization of horizontal axis tidal turbine
• [11] 王龙滟.Effectiveness of cooperative yaw control based on reinforcement learning for in-line multiple wind turbines
• [12] 王龙滟.A cost-effective CNN-BEM coupling framework for design optimization of horizontal axis tidal turbine blades
• [13] 王龙滟.A novel cost-efficient deep learning framework for static fluid–structure interaction analysis of hydrofoil in tidal turbine morphing blade
• [14] 王龙滟.Flow reconstruction from sparse sensors based on reduced-order autoencoder state estimation
• [15] 王龙滟.Study of cooperative wake control for multiple wind turbines under variable wind speeds/directions