三维气的流量控制通过多剂增强学习过渡到流
Pol Suárez1, Francisco Alcántara-Ávila2, Jean Rabault3
1FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden. polsm@kth.se.
Communications engineering
|June 18, 2025
概括
深度增强学习发现了用于3D气的新型阻力降低方法. 这种方法实现了16%的阻力降低,通过稳定起伏结构,优于传统策略.
科学领域:
- 流体动力学 流体动力学
- 空气动力学 航空动力学
- 机器学习 机器学习
背景情况:
- 对于3D悬崖体来说,主动流量控制是复杂的,但对于工业来说至关重要.
- 发现有效的阻力降低策略仍然是一个重大挑战.
研究的目的:
- 探索深度强化学习,用于3D气上的新型阻力降低策略.
- 为了研究在雷诺兹数 (100-400) 范围内的流量控制.
主要方法:
- 实现了使用零净质量流量喷射器的高维活性流量控制设置.
- 结合了计算流体动力学解决器与多代理强化学习框架 (近接政策优化).
主要成果:
- 在雷诺兹数量400时实现了高达16%的阻力降低.
- 与传统的周期性控制方法相比,其表现优越.
- 适当的直角分解揭示了一个稳定的唤醒结构,带有延长的循环气泡.
结论:
- 深度强化学习成功地确定了3D气的有效主动流量控制策略.
- 这项研究是第一个在3D气上进行RL训练以控制流量.
- 这些发现为控制复杂的流提供了一条途径.
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