Currently, in the design standards for environmental sampling to assess long-term fatigue damage, the grid-based sampling method is used to scan a rectangular grid of meteorological inputs. However, the required simulation cost increases exponentially with the number of environmental parameters, and considerable time and effort are required to characterise the statistical uncertainty of offshore wind turbine (OWT) systems. In this study, a K-type jacket substructure of an OWT was modelled numerically. Time rather than frequency-domain analyses were conducted because of the high nonlinearity of the OWT system. The Monte Carlo (MC) sampling method is well known for its theoretical convergence, which is independent of dimensionality. Conventional grid-based and MC sampling methods were applied for sampling simulation conditions from the probability distributions of four environmental variables. Approximately 10,000 simulations were conducted to compare the computational efficiencies of the two sampling methods, and the statistical uncertainty of the distribution of fatigue damage was assessed. The uncertainty due to the stochastic processes of the wave and wind loads presented considerable influence on the hot-spot stress of welded tubular joints of the jacket-type substructure. This implies that more simulations for each representative short-term environmental condition are required to derive the characteristic fatigue damage. The characteristic fatigue damage results revealed that the MC sampling method yielded the same error level for Grids 1 and 2 (2443 iterations required for both) after 1437 and 516 iterations for K- and KK-joint cases, respectively. This result indicated that the MC method has the potential for a high convergence rate.

The objective of optimal sensor placement in a dynamic system is to obtain a sensor layout that provides as much information as possible for structural health monitoring (SHM). Whereas most studies use only one modal assurance criterion for SHM, this work considers two additional metrics, signal redundancy and noise ratio, combining into three optimization objectives: Linear independence of mode shapes, dynamic information redundancy, and vibration response signal strength. A modified multiobjective evolutionary algorithm was combined with particle swarm optimization to explore the optimal solution sets. In the final determination, a multiobjective decision-making (MODM) strategy based on distance measurement was used to optimize the aforementioned objectives. We applied it to a reduced finite-element beam model of a reference building and compared it with other selection methods. The results indicated that MODM suitably balanced the objective functions and outperformed the compared methods. We further constructed a three-story frame structure for experimentally validating the effectiveness of the proposed algorithm. The results indicated that complete structural modal information can be effectively obtained by applying the MODM approach to identify sensor locations.

This study predicted the motion response and power performance of a floating wind turbine system equipped with a semisubmersible Taida platform, an IEA 15 MW wind turbine, and a 3 × 2 mooring design in the Hsinchu offshore area in the Taiwan Strait. The hydrodynamic properties were calculated using ANSYS-AQWA and STAR-CCM+. The motion equations were solved by OrcaFlex to obtain the motion response and generator power, as well as the dynamics of the mooring system and aerodynamics of the wind turbine. The waves were assumed to share the same direction as the wind. This study compared the mean values and standard deviations of the motion response, generator power, and mooring line tension between the potential- and viscous-flow approaches by considering the combination of seven wind directions and four current directions under two wave conditions in the Hsinchu offshore area. The numerical prediction shows that the viscous effect has a larger impact on the hydrodynamic properties in the heave, roll, and pitch motions. The angle between the leading mooring line of the system and dominant wind direction in the Taiwan Strait, which comes from the northeast, should be from 120° to 180° in order to deliver a relatively favorable performance of the system.

When solving real-world problems with complex simulations, utilizing stochastic algorithms integrated with a simulation model appears inefficient. In this study, we compare several hybrid algorithms for optimizing an offshore jacket substructure (JSS). Moreover, we propose a novel hybrid algorithm called the divisional model genetic algorithm (DMGA) to improve efficiency. By adding different methods, namely particle swarm optimization (PSO), pattern search (PS) and targeted mutation (TM) in three subpopulations to become “divisions,” each division has unique functionalities. With the collaboration of these three divisions, this method is considerably more efficient in solving multiple benchmark problems compared with other hybrid algorithms. These results reveal the superiority of DMGA in solving structural optimization problems.

Typhoons, such as Soudelor, which caused the collapse of several onshore wind turbines in 2015, pose a considerable challenge to Taiwan’s wind energy industry. In this study the characteristics of the aerodynamic loads acting on a wind turbine due to wind gusts in a typhoon are studied with a view to providing a proper definition of the S-Class wind turbine proposed in International Electrotechnical Commission (IEC) 61400-1. Furthermore, based on analysis of wind data during typhoons, as obtained from the meteorological mast in the Zhangbin coastal area, an extreme wind speed and gust model corresponding to the typhoon wind conditions in Taiwan are herein proposed. Finally, the flow fields around a parked wind turbine experiencing both an unsteady gust and a steady extreme wind were simulated by a numerical approach. Numerical results show that the aerodynamic shear force and overturning moment acting on the target wind turbine in a steady wind are significantly lower than those under an unsteady gust. The gust-induced amplification factors for aerodynamic loadings are then deduced from numerical simulations of extreme wind conditions.