基于深度学习的超表面设计,用于智能冷却航天器
Ayman Negm1,2, Mohamed H Bakr1, Matiar M R Howlader1
1Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada.
Nanomaterials (Basel, Switzerland)
|December 8, 2023
概括
我们开发了一个快速的人工智能模型来设计可重新配置的超表面. 这种方法可以有效设计使用二氧化相变的航天器适应式冷却系统.
科学领域:
- 纳米光子学 纳米光子学
- 超材料是指一种超材料.
- 人工智能的人工智能
背景情况:
- 可重新配置的元表面对于适应性纳米光子应用,如航天器热管理至关重要.
- 这些复杂结构的当前设计方法可能耗时且计算密集.
研究的目的:
- 引入一种新的,快速建模方法,用于设计可调和可重新配置的地表结构.
- 为了证明这种方法在为航天器创建被动自适应冷却表面的实用性.
主要方法:
- 一个卷积深度学习网络将超表面结构模型作为多层图像张量器.
- 操作波长被纳入作为输入,以解决维度不匹配的问题.
- 一个feed-forward替代模型与模式搜索优化集成.
主要成果:
- 深度学习模型准确地用一个小的训练数据集预测了 metasurface 的反应.
- 一个有图案的二氧化瓦纳超表面实现了28%的涂层厚度降低和0.43的辐射率对比度.
- 设计方法成功地产生了多个独特的模式,满足了设计目标.
结论:
- 拟议的人工智能驱动的设计方法加速了可重新配置的元表面的开发.
- 这种方法为被动航天器冷却应用提供了可行的解决方案.
- 该方法可扩展到广泛的纳米光子应用.
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