Limited by scaling effects, the physical model tests of FOWTs (floating offshore wind turbines) cannot simulate the aerodynamic loads on rotors correctly. To solve this problem, the real-time hybrid model tests in wind tunnels were developed and provided a feasible solution for the aerodynamic simulation. To perform the wind tunnel tests, the design of aerodynamic equivalent rotor models is most critical. In this study, an innovative methodology of aerodynamic equivalent design for the wind turbine rotors is developed based on GA (genetic algorithm). The NREL (National Renewable Energy Laboratory) 5 MW and DTU (Technical University of Denmark) 10 MW rotors are employed for the case studies to validate the proposed methodology. According to the results, the model-scale aerodynamic thrust performance can be accurately matched with the prototype in the entire region between cut-in and cut-out wind speeds, which allows the rotor model to provide correct thrust at different wind speeds. The variance of the aerodynamic torque with the wind speeds for the developed model is also in good agreement with the prototype, which could be beneficial for the design of the model-scale active pitch control strategy. Moreover, the applicability of the fitness functions of GA is discussed.

The foundation structure of offshore substation platforms is complex and has numerous degrees of freedom. The computational efficiency of dynamic response analysis using traditional time-domain methods is often relatively low. To facilitate the dynamic response analysis of offshore substation platform foundation structures with large degrees of freedom (DOFs), this paper proposes a method for dynamic response analysis based on modal truncation, which involves conducting a modal analysis and decoupling the vibration system matrix based on the modal shapes. Additionally, it analyzes the external load spectrum and determines the modal-truncation range through the transfer relationship between the external load frequency and the modal response. This approach reduces the size of the dynamic response analysis matrix, thereby significantly improving the computational efficiency of the dynamic response analysis of large-DOF offshore substation platform foundations. A six-DOF mass-damping-spring system and a finite element model of an offshore substation platform foundation were used to test the proposed method. The proposed method reduces the number of decoupled equations used to analyze the offshore substation platform foundation from 312 to 9. It was compared with the Newmark-β method, and the results show that the proposed method aligns well with the Newmark-β method in terms of calculation results, with computational efficiency improved by approximately 100 times.

This paper proposes an innovative algorithm for forecasting the motion response of floating offshore wind turbines by employing force-to-motion transfer functions and state-space models. Traditional numerical integration techniques, such as the Newmark-β method, frequently struggle with inefficiencies due to the heavy computational demands of convolution integrals in the Cummins equation. Our new method tackles these challenges by converting the problem into a system output calculation, thereby eliminating convolutions and potentially enhancing computational efficiency. The procedure begins with the estimation of force-to-motion transfer functions derived from the hydrostatic and hydrodynamic characteristics of the wind turbine. These transfer functions are then utilized to construct state-space models, which compactly represent the system dynamics. Motion responses resulting from initial conditions and wave forces are calculated using these state-space models, leveraging their poles and residues. We validated the proposed method by comparing its calculated responses to those obtained via the Newmark-β method. Initial tests on a single-degree-of-freedom (SDOF) system demonstrated that our algorithm accurately predicts motion responses. Further validation involved a numerical model of a spar-type floating offshore wind turbine, showing high accuracy in predicting responses to both regular and irregular wave conditions, closely aligning with results from conventional methods. Additionally, we assessed the efficiency of our algorithm over various simulation durations, confirming its superior performance compared to traditional time-domain methods. This efficiency is particularly advantageous for long-duration simulations. The proposed approach provides a robust and efficient alternative for predicting motion responses in floating offshore wind turbines, combining high accuracy with improved computational performance. It represents a promising tool for enhancing the development and evaluation of offshore wind energy systems.