Abstract
The present work focused on the optimization of offshore wind turbine structure which can sustain different environmental conditions and is of the least cost. Size and topology optimization is carried out for the jacket structure from the National Renewable Energy Laboratory (NREL) [used in the Offshore Code Comparison Collaboration Continuation (OC4) project] by using teaching learning-based optimization (TLBO) algorithm and genetic algorithm (GA). The optimization process is carried out in Matlab along with the time-dependent dynamic wind turbine simulation with the aerodynamic, hydrodynamic and structural forces in the fatigue, aerodynamics, structures, and turbulence code (FAST) from NREL. This is an innovative process which can be used to substitute the time-consuming construction of a wind turbine for its analysis. In this work, both static and dynamic analyses are carried out for simultaneous size and topology optimization. The forces applied to the structure are realistic in nature and fatigue analysis is carried out to ensure that the structure does not fail during its design life. This ensures that the simulation is more accurate and realistic as compared with other analysis. The results showed that the TLBO algorithm is effective compared to GA in terms of size and topology optimization. Further, the other state-of-the art algorithms from the Congress on Evolutionary Computation (CEC) such as differential evolution, LSHADE, multi-operator EA-II, effective butterfly optimizer, and unified differential evolution are also implemented and the comparative results of all the algorithms are presented.
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