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.
This study evaluated, by time-domain simulations, the fatigue life of the jacket support structure of a 3.6 MW wind turbine operating in Fuhai Offshore Wind Farm. The long-term statistical environment was based on a preliminary site survey that served as the basis for a convergence study for an accurate fatigue life evaluation. The wave loads were determined by the Morison equation, executed via the in-house HydroCRest code, and the wind loads on the wind turbine rotor were calculated by an unsteady BEM method. A Finite Element model of the wind turbine was built using Beam elements. However, to reduce the time of computation, the hot spot stress evaluation combined FE-derived Closed-Form expressions of the nominal stresses at the tubular joints and stress concentration factors. Finally, the fatigue damage was assessed using the Rainflow Counting scheme and appropriate SN curves. Based on a preliminary sensitivity study of the fatigue damage prediction, an optimal load setting of 60-min short-term environmental conditions with one-second time steps was selected. After analysis, a sufficient fatigue strength was identified, but further calculations involving more extensive long-term data measurements are required in order to confirm these results. Finally, this study highlighted the sensitivity of the fatigue life to the degree of fluctuation (standard deviation) of the wind loads, as opposed to the mean wind loads, as well as the importance of appropriately orienting the jacket foundations according to prevailing wind and wave conditions.
Recently, Taiwan started to evaluate the potential of wind energy production on its West coast. The concern was raised about employing existing solutions validated by experience for mild environment regions to Taiwan which is frequently subject to Typhoon. This study investigated the strength under typhoon condition of two offshore wind farm units: a meteorological mast supported by a monopile and a 3.6 MW wind turbine supported by a 4-leg jacket. Especially, two critical load cases were analyzed. First, the study provided a simplified approach to evaluate the wave run-up load on a monopile. The dynamic structure response of the meteorological mast evaluated through finite element analyses showed that large vibrations excited the tower after the slamming. In a second time, the study evaluated the extreme wind loads exerted on the blades of the parked wind turbine considering a blade pitch control fault. As a result, for a constant gust wind speed of 70 m/s, the loads at the nacelle increased tremendously by approximately 220% compared to the parked wind turbine without fault condition.
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.
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