基于强化学习的复合薄膜的反向设计用于航天器智能热控制
Yongxing Chen1, Haining Ji1, Peng Long1
1School of Physics and Optoelectronics, Xiangtan University, Xiangtan, Hunan, 411105, P. R. China. sdytjhn@126.com.
Physical chemistry chemical physics : PCCP
|March 28, 2025
概括
本研究介绍了一种用于航天器热控制的新型复合膜,利用机器学习来优化动态热发射器和太阳能反射器. 智能薄膜显示了可调节的显著发射率和冷却功率,提高了航天器的性能和寿命.
科学领域:
- 材料科学 材料科学 材料科学
- 航空航天工程 航空航天工程
- 计算科学 计算科学
背景情况:
- 有效的热控制对于航天器的有效载荷性能和寿命至关重要.
- 设计热控制复合膜的传统方法面临着局限性.
- 二氧化瓦纳 (VO2) 为智能热管理提供有利的热色特性.
研究的目的:
- 设计用于航天器热控制的先进复合膜.
- 使用机器学习集成动态热发射器和太阳能反射器.
- 为了满足太空任务的苛刻热控制要求.
主要方法:
- 开发了一个强化学习优化框架,使用转移矩阵方法和深度Q学习.
- 优化了多个Fabry-Pérot共振器堆叠结构,用于动态热发射器.
- 设计了一个太阳反射器,在太阳波长频段中吸收低.
主要成果:
- 通过三共振器结构实现了接近10微米的宽可调节发射率范围 (0.939).
- 复合膜具有较低的太阳吸收率 (0.180) 和高可调节的发射率 (0.806).
- 模拟的冷却功率从 -183.42 W m-2 转移到 83.13 W m-2 在相位过渡中.
结论:
- 开发的智能热控制复合膜显示了航天器应用的巨大潜力.
- 这部电影在广泛的事件角度中保持了性能.
- 机器学习驱动的设计增强了航天器热管理系统的功能.
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