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RESEARCH FIELDS AND INTERESTS
Offshore Renewable Energy: numerical and experimental study of offshore wind turbines (both bottom-fixed and floating), wave energy converters (oscillating bodies and overtopping devices), tidal turbines and combined concepts; integrated methods for global dynamic load and response analysis of offshore wind turbines; hierarchical methods for local response analysis of wind turbine drivetrains; design and analysis of floating structures to support large-scale (5-15 MW) wind turbines; modeling and analysis of offshore wind turbines with geared drivetrain, direct drive and hydraulic transmission; mooring system design and analysis for offshore renewable energy platforms, including shallow waters.
Marine Operations: operational and safety criteria for marine operations related to offshore wind turbine transport, installation and access for maintenance and repair; numerical modeling and time-domain simulation of installation operation for offshore wind turbine components (such as crane operations for installing wind turbine blades, monopile, jacket and spar foundations); weather window and operability assessment using response-based criteria; assessment of uncertainties in numerical modelling and weather forecast and their effects on decision-making for marine operations.
Structural Mechanics and Dynamics: dynamic analysis of offshore structures using finite element methods; coupled mooring analysis; fatigue assessment of offshore structures; numerical simulation of ship collision and grounding.
Stochastic Analysis: frequency-domain cycle counting methods for fatigue analysis of wide-band processes; extreme value prediction and fatigue analysis of non-Gaussian processes; stochastic modeling of random waves, including spatially inhomogeneous waves; short-term and long-term statistics of wind and waves and their induced loads/responses; contour line or surface methods for long-term extreme responses of marine structures under separate or simultaneous wind and wave loads.
Machine Learning Approaches and Data-driven Models Applied in Marine Technology: forecast of short-term (1-hour to 1-day ahead) wind and wave conditions using machine-learning approaches for decision-making for execution of marine operations; forecasting of short-term (1-3 wave periods ahead) motions of vessels and wave energy converters for control purpose; machine learning approaches for fault/failure detection and diagnosis for wind turbine drivetrain/blade pitch actuator and marine structural components (such as mooring lines).
Reliability and Risk Analysis: uncertainty modeling and structural reliability assessment of offshore oil and gas platforms as well as renewable energy platforms; fatigue reliability methods applied to mechanical components (such as gearbox in wind turbines); fatigue reliability analysis of offshore wind turbine foundations (such as jacket) for inspection and maintenance planning; overload and fatigue reliability of mooring system.
Offshore Renewable Energy: numerical and experimental study of offshore wind turbines (both bottom-fixed and floating), wave energy converters (oscillating bodies and overtopping devices), tidal turbines and combined concepts; integrated methods for global dynamic load and response analysis of offshore wind turbines; hierarchical methods for local response analysis of wind turbine drivetrains; design and analysis of floating structures to support large-scale (5-15 MW) wind turbines; modeling and analysis of offshore wind turbines with geared drivetrain, direct drive and hydraulic transmission; mooring system design and analysis for offshore renewable energy platforms, including shallow waters.
Marine Operations: operational and safety criteria for marine operations related to offshore wind turbine transport, installation and access for maintenance and repair; numerical modeling and time-domain simulation of installation operation for offshore wind turbine components (such as crane operations for installing wind turbine blades, monopile, jacket and spar foundations); weather window and operability assessment using response-based criteria; assessment of uncertainties in numerical modelling and weather forecast and their effects on decision-making for marine operations.
Structural Mechanics and Dynamics: dynamic analysis of offshore structures using finite element methods; coupled mooring analysis; fatigue assessment of offshore structures; numerical simulation of ship collision and grounding.
Stochastic Analysis: frequency-domain cycle counting methods for fatigue analysis of wide-band processes; extreme value prediction and fatigue analysis of non-Gaussian processes; stochastic modeling of random waves, including spatially inhomogeneous waves; short-term and long-term statistics of wind and waves and their induced loads/responses; contour line or surface methods for long-term extreme responses of marine structures under separate or simultaneous wind and wave loads.
Machine Learning Approaches and Data-driven Models Applied in Marine Technology: forecast of short-term (1-hour to 1-day ahead) wind and wave conditions using machine-learning approaches for decision-making for execution of marine operations; forecasting of short-term (1-3 wave periods ahead) motions of vessels and wave energy converters for control purpose; machine learning approaches for fault/failure detection and diagnosis for wind turbine drivetrain/blade pitch actuator and marine structural components (such as mooring lines).
Reliability and Risk Analysis: uncertainty modeling and structural reliability assessment of offshore oil and gas platforms as well as renewable energy platforms; fatigue reliability methods applied to mechanical components (such as gearbox in wind turbines); fatigue reliability analysis of offshore wind turbine foundations (such as jacket) for inspection and maintenance planning; overload and fatigue reliability of mooring system.
研究兴趣
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Renewable and Sustainable Energy Reviews (2024): 114547
Energypp.130885, (2024)
Renewable Energypp.120291, (2024)
RENEWABLE ENERGY (2024): 119678-119678
Environmental science and pollution research internationalno. 33 (2023): 79654-79675
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