深度强化学习的应用,用于通过合成喷气式飞机在角翼周围的空气动力学控制
Nadia Ghezaiel Hammouda1, Rashid Khan2, Loghman Mostafa3
1Department of Software Engineering, College of Computer Science and Engineering, University of Hail, Ha'il City 81451, Saudi Arabia.
Scientific reports
|December 1, 2025
概括
这项研究使用深度强化学习 (DRL) 进行空气动力学控制. 通过稳定空气流量,DRL框架优化了提升和阻力,证明了航空航天应用的强大战略.
科学领域:
- 空气动力学 航空动力学
- 流体动力学 流体动力学
- 人工智能的人工智能
背景情况:
- 积极的流量控制对于优化空气动力学性能至关重要.
- 合成喷气驱动为流量操纵提供了一个有前途的方法.
- 深度强化学习 (DRL) 为复杂的控制任务提供了一种新的方法.
研究的目的:
- 开发和评估一个DRL框架,用于空气动力学控制弱可压缩的流量围绕一个角度的气翼.
- 使用合成喷气驱动来优化起重和拖动特性.
- 通过整合压力和速度数据来提高控制精度和学习效率.
主要方法:
- 使用深度Q网络 (DQN) 架构实现了一个DRL框架.
- 经纪人使用压力和速度传感器数据在300个插曲中进行了训练.
- 对传统DQN,双DQN和对决DQN架构进行了比较分析.
主要成果:
- 压力和速度数据的整合改善了总奖励和控制性能.
- 该DRL剂有效地减少了流的流失,并稳定了气形状.
- 实现了最小化的起重波动和降低阻力,Dueling DQN表现出卓越的性能.
- 证明了稳定的收和有效的后续稳定.
结论:
- 提出的基于DRL的方法为主动流量控制提供了一个高效和强大的策略.
- 这种方法显示出在可压缩空气动力学系统和未来航空航天工程中应用的巨大潜力.
- 通过智能流量控制,DLR提供了一种强大的工具来优化空气动力学性能.
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