通过联合前向-反向深度学习,精确反向设计宽带太阳能元材料吸收器
Qihang Wu1,2, Zhiming Deng1,2, Cong Zeng1,2
1College of Ocean Information Engineering, Jimei University, Xiamen 361021, China.
我们开发了一个深度学习框架,用于设计高效的太阳能吸收器. 这种方法加速了超材料结构的优化,实现了太阳能热应用的高吸收性.
科学领域:
- 材料科学 材料科学 材料科学
- 光学是什么?光学是什么?光学是什么?
- 人工智能的人工智能
背景情况:
- 设计宽带,高效的太阳能吸收器是复杂的,因为逆向的设计挑战.
- 传统的方法与从光学特性到物理结构的错误映射作斗争.
研究的目的:
- 提出一个共同的前向逆向深度学习框架,以快速和准确地优化多层元材料吸收器.
- 为了解决一对多的模糊性,并提高逆向设计中的预测准确性.
主要方法:
- 集成了一个反向网络 (修改Swin变压器) 与前向代理 (多层感知器).
- 在一个以一致性驱动的循环中进行员工端到端的培训,以共同优化.
- 优化W/SiO2高压元材料堆与TiO2/SiO2反射涂层.
主要成果:
- 在400-1750nm太阳光谱中达到97.4%的平均吸收率.
- 证明极化无敏性和广角性能高达60°的发生率.
- 制造吸收器在自然阳光下达到86.3°C.
结论:
- 联合前向逆向深度学习框架加速了高性能太阳能热元材料的设计.
- 这种方法为智能材料设计提供了一个强大而可扩展的范式.
- 优化的吸收器显示了太阳能能源应用的巨大潜力.
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