Wind-wave hybrid power generation systems have the potential to become a significant source of affordable renewable energy. However, their strong interactions with both wind- and wave-induced forces raise a number of technical challenges for modelling. The present study undertakes a numerical investigation on multi-body hydrodynamic interaction between a wind-wave hybrid floating platform and multiple wave energy converters (WECs) in a frequency domain. In addition to the exact responses of the platform and the WECs, the power take-off (PTO) mechanism was taken into account for analysis. The coupled hydrodynamic coefficients and wave exciting forces were obtained from WAMIT, the 3D diffraction/radiation solver based on the boundary element method. The overall performance of the multiple WECs is presented and compared with the performance of a single isolated WEC. The analysis showed significant differences in the dynamic responses of the WECs when the multi-body interaction was considered. In addition, the PTO damping effect made a considerable difference to the responses of the WECs. However, the platform response was only minimally affected by PTO damping. With regard to energy capture, the interaction effect of the designed multiple WEC array layout is evaluated. The WEC array configuration showed both constructive and destructive effects in accordance with the incident wave frequency and direction.

The effect of hydrodynamic performance of the wave energy converter (WEC)–rotor based on linear potential flow theory due to nonlinear viscous damping was investigated. Free decay tests were conducted using computational fluid dynamics (CFD) to obtain the viscous damping moment. The commonly used procedure for obtaining the damping moment is based on peak amplitudes which normally require a long time history records. Such long free decay records may not be possible in nodding WEC rotor due high damping. The energy method proposed by Bass and Haddara requires only the short and full range of the recorded data. This method provides sufficiently good results when the bodies have high damping. The method equates the rate of change of the total energy of a body undergoing free rolling/pitching to the rate of energy dissipated by the damping. The present study adopts a similar methodology for estimating the linear and linear plus quadratic damping. To incorporate the nonlinear viscous damping moment in the linear equation of motion, an equivalent linearization concept is used without neglecting the nonlinear damping effects. The hydrodynamic coefficients obtained from the linear potential flow theory, nonlinear viscous damping moment from the energy method and estimated PTO damping are used to solve the equation of motion of the WEC rotor. The estimated pitch free decay data shows good agreement with the simulated CFD results when compared to the linear viscous damping moment and better agreement is obtained with linear plus quadratic viscous damping moment. The regular and irregular wave analyses show that a considerable effect on the hydrodynamic performance of the WEC rotor is observed when the linear and linear plus quadratic viscous damping are included.

The incorporation of machine learning (ML) has yielded substantial benefits in detecting nonlinear patterns across a wide range of applications, including offshore engineering. Existing ML works, specifically supervised regression models, have not undergone exhaustive scrutiny, and there are no potential or concurrent models for improving the performance of wave energy converter (WEC) devices. This study employs supervised regression ML models, including multi-layer perceptron, support vector regression, and XGBoost, to optimize the geometric aspects of an asymmetric WEC inspired by Salter’s duck, based on key parameters. These important parameters, the ballast weight and its position, vary along a guided line within the available geometric resilience of the asymmetric WEC. Each supervised regression ML model was fine-tuned through hyperparameter optimization using Grid cross-validation. When evaluating the performance of each ML model, it became evident that the tuned hyperparameters of XGBoost led to predictions that strongly aligned with the actual values compared to other models. Furthermore, the study extended to assess the performance of the optimized WEC at the designated deployment test site location.