深度学习辅助的活性超材料与热增强的热传输
Peng Jin1, Liujun Xu2,3, Guoqiang Xu2
1Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory of Micro and Nano Photonic Structures (MOE), Fudan University, Shanghai, 200438, China.
Advanced materials (Deerfield Beach, Fla.)
|October 23, 2023
概括
深度学习为新的热增强热扩散超材料提供动力. 这些自适应性超材料自动调整热功能的先进应用,克服当前自适应性材料的局限性.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术纳米技术
- 人工智能的人工智能
背景情况:
- 有效的热管理对于像辐射冷却和智能可穿戴设备这样的先进技术至关重要.
- 目前适应性超材料面临的挑战包括狭窄的温度范围,异构性和手动控制.
研究的目的:
- 引入以深度学习为动力的热增强热扩散超材料,用于自适应性热管理.
- 克服现有的适应性超材料的局限性,使自发和可调节的热反应成为可能.
主要方法:
- 使用深度学习算法的热增强热扩散元材料的开发.
- 对超材料的热性能和适应性进行模拟和实验验证.
- 设计了两个半端设备,展示了与同位素材料的按需适应性.
主要成果:
- 根据环境温度证明了基于环境温度的热功能的自发和持续调整.
- 无论外部热场方向如何,都能实现强大的热性能.
- 通过模拟和实验验证实了卓越的性能.
结论:
- 开发的超材料为智能热扩散提供了一个框架,可以根据需求进行适应.
- 这种方法使得具有广泛工作温度和自发反应的同源材料成为可能.
- 这项技术有可能在动态和复杂的环境中得到更广泛的应用.
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