Platform motions induced by waves pose a challenge to accurately predict the aerodynamic performance of floating offshore wind turbines (FOWTs). In view of this, the power performance and wake structure of FOWTs under platform pitch, surge, and their combined motions were investigated in this paper, using the computational fluid dynamics software, STAR-CCM+, with overset meshing and rigid body motion techniques. First, the simulation cases in single and same-phase combined motions with different amplitudes and frequencies were performed. Afterward, the approach of calculating the phase difference between pitch and surge motions was proposed to investigate the influence of the combined motion with phase difference on the aerodynamic performance. Results show that the increment of amplitude and frequency augments the mean power output and aggravates the power fluctuation in single and same-phase combined motions. The intensity of power variation under combined motion with a phase difference is weakened at 0.1 Hz compared to the single motion, while enhanced at 0.2 Hz, showing a different influence law on the aerodynamic performance. In addition, this paper established the power fluctuation table based on real sea states of Shidao in China, providing a certain reference for the controller design in this sea area.

Accurate prediction of the dynamic responses of the floating offshore wind turbine (FOWT) under the blade pitch motion is quite challenging because of the strong nonlinear effects. In this study, a fully coupled and highly elaborated model was established based on the computational fluid dynamics, with the dynamic fluid body interaction method. The multi-stage movements consisting of the six degrees of freedom motions of the platform, the rotation of the rotor, and the blade pitch motion were defined by the superposition motion technologies. The blade pitch control module was created through the user-defined function to regulate the blade pitch motion. Then, several coupled dynamic simulations of the full-configuration DeepCwind floating wind turbine system were performed in power production, shutdown, and startup cases. The simulation results in the power production case indicate that the blade pitch motion decreases the generated aerodynamic loads and amplifies the response amplitude of the platform as negative damping is introduced in the FOWT system. The simulation results in the shutdown and startup cases indicate that the extreme motion responses are enlarged, and the mooring line tension oscillates dramatically when it is in high-tension states. In addition, the nonlinear interference effects in the unsteady flow fields, such as the shedding vortices broken by the blade pitch motion, are visualized and investigated in detail.

The present study aims to carry out a comparative Multi-Objective Optimization (MOO) of a 10 MW FOWT semi-submersible using three different metaheuristic optimization techniques and a sophisticated approach for optimizing a floating platform. This novel framework enables highly efficient 3D plots, an optimization loop, and the automatic and comparative output of solutions. Python, the main interface, integrated PyMAPDL and Pymoo for intricate modeling and simulation tasks. For this case study, the ZJUS10 Floating Offshore Wind Turbine (FOWT) platform, developed by the state key laboratory of mechatronics and fluid power at Zhejiang University, was employed as the basis. Key criteria such as platform stability, overall structural mass, and stress were pivotal in formulating the objective functions. Based on a preliminary study, the three metaheuristic optimization algorithms chosen for optimization were Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO). Then, the solutions were evaluated based on Pareto dominance, leading to a Pareto front, a curve that represents the best possible trade-offs among the objectives. Each algorithm’s convergence was meticulously evaluated, leading to the selection of the optimal design solution. The results evaluated in simulations elucidate the strengths and limitations of each optimization method, providing valuable insights into their efficacy for complex engineering design challenges. In the post-processing phase, the performances of the optimized FOWT platforms were thoroughly compared both among themselves and with the original model, resulting in validation. Finally, the ACO algorithm delivered a highly effective solution within the framework, achieving reductions of 19.8% in weight, 40.1% in pitch, and 12.7% in stress relative to the original model.

Complicated loads encountered by floating offshore wind turbines (FOWTs) in real sea conditions are crucial for future optimization of design, but obtainin