在红外成像中揭示隐藏的2D大气流强度场,来自流效应
Yadong Wang1, Darui Jin1, Junzhang Chen1
1Image Processing Center, Beihang University, Beijing, China.
Nature computational science
|January 4, 2024
概括
这项研究引入了一种使用红外成像来测量二维大气流强度场的新方法. 物理增强型合作学习 (PBCL) 框架准确量化了流,克服了以前的测量挑战.
科学领域:
- 流体动力学 流体动力学
- 大气科学 大气科学
- 光学遥感器 遥感器 遥感器
背景情况:
- 大气和工业流体中的流,由于其复杂,随机和异型性质,给分析和测量带来了重大挑战.
- 测量大气流强度的现有方法,如多普勒雷达或激光,只提供空间整合值,而不是详细的二维场.
- 直接量化大气中二维 (2D) 流强度场仍然是一个困难的问题.
研究的目的:
- 利用红外成像开发一种用于量化二维流强度场的新方法.
- 为此目的引入和验证一个物理增强的合作学习 (PBCL) 框架.
- 展示成像技术在测量复杂物理量场中的潜力.
主要方法:
- 开发一个物理增强合作学习 (PBCL) 框架,旨在从红外图像中量化二维流强度.
- 构建一个包括137,336个红外图像及其相应的二维流强度场的综合数据集,用于培训和验证.
- 应用合作学习原则,同时学习流强度和减轻流引起的图像扭曲.
主要成果:
- 该PBCL框架成功地从红外图像中量化了2D流强度场.
- 在PBCL框架内的合作学习显然提高了流测量的性能.
- 该方法有效地抑制了红外图像中存在的不利流效应,提高了测量精度.
结论:
- 红外成像与先进的机器学习相结合,为直接2D大气流强度场测量提供了可行的解决方案.
- 拟议的物理增强协作学习 (PBCL) 框架显示了准确量化流的巨大潜力.
- 这项研究强调了成像技术在复杂环境中测量物理量场的更广泛适用性.
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