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. Firstly, the open-source multiphysics libraries SPHinXsys and Simbody are employed to establish the numerical environment for wave interaction with OWSCs. Subsequently, a comparative analysis of three DRL algorithms-proximal policy optimization (PPO), twin delayed deep deterministic policy gradient (TD3), and soft actor-critic (SAC)-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 SAC 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 (3D) study, with an improvement of 22.54% in energy harvesting. 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.

翻译：振荡波涌转换器（OWSC）的非线性阻尼特性对动力输出系统性能具有显著影响。本研究提出了一种将深度强化学习（DRL）与OWSC数值模拟相结合的框架，旨在识别变化波浪条件下的最优自适应阻尼策略，从而提高波浪能捕获效率。首先，采用开源多物理场库SPHinXsys和Simbody建立波浪与OWSC相互作用的数值环境。随后，通过OWSC与规则波相互作用的二维（2D）数值研究，对三种DRL算法——近端策略优化（PPO）、双延迟深度确定性策略梯度（TD3）和柔性演员-评论家（SAC）——进行了对比分析。结果表明，人工神经网络能够捕捉波-结构相互作用的非线性特征，并提供高效的PTO策略。值得注意的是，SAC算法展现出卓越的鲁棒性和准确性，实现了波浪能捕获效率10.61%的提升。此外，在二维环境中训练的策略成功应用于三维（3D）研究，能量捕获效率提升了22.54%。研究还表明，对于复杂的不规则波浪，能量捕获效率提高了6.42%。然而，对于复杂的双OWSC系统，仅优化阻尼特性不足以提升能量捕获效率。