Paper Publications

• [1] 王龙滟.Sparse wind turbine wake reconstruction using physics-informed proper orthogonal decomposition
• [2] 王龙滟.耦合风速测量的风力机时空尾流重构
• [3] 王龙滟.Enhanced Diagnosis of Wind Turbine Main Bearing Faults Through Fusion of Multi-source Signals with a Hybrid MTF-CNN-NSGAII Approach
• [4] 王龙滟.A novel generative approach to the parametric design and multi-objective optimization of horizontal axis tidal turbines
• [5] 王龙滟.Deep learning enhanced fluid-structure interaction analysis for composite tidal turbine blades
• [6] 王龙滟.Intelligent fault diagnosis for wind turbine main bearings through multi-source signal fusion and lightweight model design with knowledge distillation
• [7] 王龙滟.TurbineNet: Advancing tidal turbine blade hydrodynamic performance prediction with neural networks
• [8] 王龙滟.Transfer learning-enhanced fault diagnosis for wind turbine main bearings under diverse wind farm conditions
• [9] 王龙滟.Effectiveness of wake control optimization for multiple in-line wind turbines by combinatorial machine learning wake model
• [10] 王龙滟.A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements
• [11] 王龙滟.DLFSI: A deep learning static fluid-structure interaction model for hydrodynamic-structural optimization of composite tidal turbine blade
• [12] 王龙滟.Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data
• [13] 王龙滟.Super-resolution reconstruction framework of wind turbine wake: Design and application
• [14] 王龙滟.TurbineNet/FEM: Revolutionizing fluid-structure interaction analysis for efficient harvesting of tidal energy
• [15] 王龙滟.Mesh-based data-driven approach for optimization of tidal turbine blade shape