A study on the wake model for floating offshore wind turbines based on CFD simulation

To address the challenge of wake prediction caused by complex motions of floating offshore wind turbines, this paper presents a high-precision CFD numerical model based on an improved actuator disk model. A UDF (user-defined function) program is developed to accurately compute body forces under multiple motion states. The proposed method, together with four other turbulence models, is systematically compared against wind tunnel measurements. The simulation results are validated against wind tunnel tests. The Realizable k-ε model with the improved actuator disk model accurately captures the velocity and turbulence intensity distributions in the turbine wake. A Sobol sensitivity analysis is conducted on 80 single-motion cases. Results show that inflow turbulence and thrust coefficient dominate the wake characteristics. Their influence is significantly greater than that of motion parameters. The motion period has a negligible effect. Pitch, surge, and roll are identified as the dominant motion forms. A Jensen-Gaussian wake model is then proposed. This model introduces motion amplitude parameters into the traditional Gaussian distribution framework. For single-motion cases, the average prediction error is only 2.0%. This error is 3.8% to 6.6% lower than that of the conventional Jensen and BPA models. For coupled motion cases, five wake superposition models are compared. The maximum loss (ML) model achieves the highest prediction accuracy. Its average error is only 1.9%. This approach enables efficient and accurate wake simulation under multi-motion synergistic effects. This study provides a high-precision, low-cost wake prediction toolchain for the layout optimization and operational control of deep-sea offshore wind farms.