设计用于宽带声学吸收的水下涂层,使用基于强化学习的拓优化
Kee Seung Oh1, Cheeyoung Joh1, Joo Hwan Oh2
1Institute of Advanced Machines and Design, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
The Journal of the Acoustical Society of America
|February 2, 2026
概括
一种新的基于强化学习的拓优化 (RL-TO) 方法设计了先进的水下声学涂层. 这种方法实现了优异的宽带和低频声音吸收,以提高隐形能力.
科学领域:
- 材料科学 材料科学 材料科学
- 声学工程 声学工程
- 人工智能的人工智能
背景情况:
- 水下声学涂层对于降低噪声和隐形性至关重要.
- 最大限度地吸收声音是提高性能的关键.
- 传统方法在实现宽带和低频吸收方面存在局限性.
研究的目的:
- 开发一种先进的方法来设计水下声学涂层.
- 为了实现宽带和低频声音吸收.
- 通过优化声学涂层来增强隐形能力.
主要方法:
- 引入了一种基于强化学习的拓优化 (RL-TO) 方法.
- 集成的强化学习与拓优化,用于动态探索.
- 分析波导和局部共振特征的优化拓.
主要成果:
- 优化的拓显示了低频的波导和高频的局部共振.
- 在多个目标频率中,达到超过0.9的平均声音吸收系数.
- 参数研究证实了诸如波浪控制器和共振器之类的几何特征的重要性.
结论:
- RL-TO是设计高性能水下声学涂层的强大而高效的工具.
- 拟议的方法比传统方法提供了显著的进步.
- 优化的涂层在广泛的频率范围内表现出强大的性能.
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