Adaptive optimization of wave energy conversion in oscillatory wave surge converters via SPH simulation and deep reinforcement learning
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909345729478656 |
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| author | Ye, Mai Zhang, Chi Ren, Yaru Liu, Ziyuan Haidn, Oskar J. Hu, Xiangyu |
| author_facet | Ye, Mai Zhang, Chi Ren, Yaru Liu, Ziyuan Haidn, Oskar J. Hu, Xiangyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08871 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive optimization of wave energy conversion in oscillatory wave surge converters via SPH simulation and deep reinforcement learning Ye, Mai Zhang, Chi Ren, Yaru Liu, Ziyuan Haidn, Oskar J. Hu, Xiangyu Computational Engineering, Finance, and Science 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. |
| title | Adaptive optimization of wave energy conversion in oscillatory wave surge converters via SPH simulation and deep reinforcement learning |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2410.08871 |