释放潜力:利用深度学习分析来自太空的小规模水泥样本的3D微观结构
Vishnu Saseendran1, Namiko Yamamoto2, Peter J Collins3
1Department of Aerospace Engineering, The Pennsylvania State University, University Park, 16802, PA, USA. vms5575@psu.edu.
NPJ microgravity
|January 25, 2024
概括
研究人员开发了一种深度学习方法,从太空样本中重建水泥微结构. 这种技术产生了外星水泥的统计模型,这对于未来的太空建设至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 航空航天工程 航空航天工程
背景情况:
- 在现场资源利用是可持续外星建筑的关键.
- 传统的物质运输到太空是成本极高的.
- 混凝土材料对于在地球之外建造结构至关重要.
研究的目的:
- 开发一种新的方法来重建在微重力中受水化的水泥微结构.
- 利用深度学习从有限的实验数据中生成统计代表性的微观结构.
- 为了能够准确地描述用于太空应用的材料.
主要方法:
- 扫描电子显微镜 (SEM) 用于捕获硬化三酸 (C3S) 样本的微图,这些样本在微重力中受水.
- 使用深度学习框架,从2D SEM示例中重建3D微结构卷.
- 统计描述符和微CT数据用于验证重建的卷.
主要成果:
- 深度学习框架成功生成了一组统计上相关的微观结构.
- 在太空中水的C3S样品呈现出明显更高的多孔度 (~70%) 和延长的波特兰形态.
- 重建的体积准确地捕获了太空返回和地面样本的微观结构特征.
结论:
- 开发的重建方法有效地从稀疏的外星数据中建模微观结构.
- 这些发现为描述基于太空的建筑材料的机械和运输特性提供了一条途径.
- 这项研究支持在太空中长期存在人类的先进材料的开发.
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