Abstract
The nonlinear damping characteristics of the oscillating wave surge converter (OWSC) significantly impact the performance of the power take-off system. This study presents a framework by integrating deep reinforcement learning (DRL) with numerical simulations of OWSC to identify optimal adaptive damping policy under varying wave conditions, thereby enhancing wave energy harvesting efficiency. The open-source multiphysics library SPHinXsys establishes the numerical environment for wave interaction with OWSCs. Subsequently, a comparative analysis of three DRL algorithms is conducted using the two-dimensional (2D) numerical study of OWSC interacting with regular waves. The results reveal that artificial neural networks capture the nonlinear characteristics of wave–structure interactions and provide efficient PTO policies. Notably, the soft actor–critic algorithm demonstrates exceptional robustness and accuracy, achieving a 10.61% improvement in wave energy harvesting. Furthermore, policies trained in a 2D environment are successfully applied to the three-dimensional study, with an improvement of 22.54% in energy harvesting. The optimization effect becomes more significant with longer wave periods under regular waves with consistent wave height. Additionally, the study shows that energy harvesting is improved by 6.42% for complex irregular waves. However, for the complex dual OWSC system, optimizing the damping characteristics alone is insufficient to enhance energy harvesting.