弹性热能热电池:基于生成学习设计的相变合金的超高储热能力
Pengfei Dang1, Jinlong Hu2, Yuehui Xian1
1State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.
Advanced materials (Deerfield Beach, Fla.)
|January 7, 2025
概括
一个新的弹性热电池使用生成学习来设计相变合金,以实现高效的低温废热回收. 这种创新的材料设计为各种热能应用实现了卓越的热储和工作热效率.
科学领域:
- 材料科学 材料科学 材料科学
- 热力学是一种热力学.
- 人工智能的人工智能
背景情况:
- 低温废热的有效回收对于能源可持续性至关重要.
- 现有的热电池往往缺乏足够的储热能力和效率.
- 开发用于热能存储的先进材料仍然是一个重大挑战.
研究的目的:
- 开发一种使用生成式学习设计的相变合金的弹性热电池.
- 为了使低温废热的热能能够有效地储存和按需释放.
- 通过反向设计框架,加速发现具有定制热性能的材料.
主要方法:
- 为合金发现提供生成式学习支持的反向设计框架.
- 根据特定的转化特征量身定制合金组成和加工参数.
- 热电池原型的制造和测试.
主要成果:
- 在热储容量方面取得了超高的成绩,超过了现有的热电池.
- 工作到热的效率超过9%.
- 成功地将手绘的目标热流曲线转化为有形材料设计.
结论:
- 开发的弹性热电池为低温废热回收提供了一个有前途的解决方案.
- 生成式学习反向设计框架显著加快了针对量身定制的财产曲线的材料开发.
- 潜在的应用包括太阳能热收集,电动汽车和数据中心热管理.
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