CAREER: Enhancing Temperature Visualization in Boiling Fluid over Finned Surfaces using Deep Learning-Enhanced Laser-Induced Fluorescence

这个项目利用深度学习辅助的实验技术来直观地研究沸腾液体中的温度变化。沸腾换热在航空、空间探索、电动汽车、工业供热等行业中起着举足轻重的作用。尽管它意义重大，但关于沸腾换热的基本问题仍然存在，包括对独特的流动模式、温度分布、气泡尺寸和轨迹的了解。这些挑战源于对温度进行建模和可视化的困难。了解温度分布和驱动过程对于开发下一代热管理系统至关重要。通过建立复杂热传递系统的知识和计量，该项目的成果有望与工业应用相关。一个特别感兴趣的领域是提高热交换器的能效。数据中心(使用热交换器)的冷却约占美国总发电量的1%，每年造成340亿美元和1.37亿吨二氧化碳的成本。因此，探索先进的温度计量和换热器性能改进分析的新机遇，将对能源产生巨大的影响。此外，该项目通过三项任务为教育做出贡献：(1)为机器学习建模制作教育视频和练习；(2)为K-12和本科生开发结构化学习活动；以及(3)与英特尔公司合作，在非学术研究环境中启发学生。目前，了解翅片表面沸腾液体的温度变化是有限的。沸腾流体翅片表面的温度场是一种复杂的基本换热方式，对其时空变化的认识还不够深入。该研究提出了一种新的温度可视化方法，该方法集成了基于激光的诊断工具和先进的深度学习方法，能够在复杂几何形状的沸腾流体中进行测量。该项目假设，深度学习的进展可以根据稀疏的测量结果重建流体中的温度场，并纠正可视化伪影，从而能够可视化沸腾流体中的时空温度变化。如果研究成功，将极大地促进对以下热传输现象的基本认识：(1)流动-结构相互作用对温度场、热边界层和过热液层发展的影响；(2)蒸气蒸发过程中热边界层的混合行为、气泡离开表面以及微层蒸发后干点的再湿；(3)沸腾过程不同阶段翅片表面上局部换热系数的分布。更好的理解将有助于设计更有效的翅片式换热器。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project utilizes deep learning-assisted experimental techniques to visually investigate temperature changes in boiling fluids. Boiling heat transfer plays a pivotal role in various industries such as aviation, space exploration, electric vehicles, and industrial heat. Despite its significance, fundamental questions persist regarding boiling heat transfer, including an understanding of unique flow patterns, temperature distributions, bubble sizes, and trajectories. These challenges arise from difficulties in modeling and visualizing temperatures. Understanding temperature distributions and driving processes is crucial for the development of next-generation thermal management systems. The outcomes of this project are expected to be pertinent to industrial applications by establishing knowledge and metrology for complex heat transfer systems. One specific area of interest is enhancing the energy efficiency of heat exchangers. Cooling for data centers (that use heat exchangers) accounts for approximately 1% of all electricity produced in the US, resulting in a cost of $34 billion and 137 million metric tons of carbon dioxide annually. Therefore, exploring new opportunities in advanced temperature-metrology and analysis for heat exchanger performance improvements will have a tremendous impact on energy resources. Additionally, the project contributes to education through three tasks: (1) creating educational videos and exercises for machine learning modeling; (2) developing structured learning activities for K-12 and undergraduate students; and (3) collaborating with Intel Corporation to inspire students in non-academic research settings.Understanding temperature changes in boiling fluids over finned surfaces is currently limited. There is a lack of understanding regarding the spatiotemporal variation of temperature field in boiling fluids over finned surface, which represent a complex fundamental mode of heat transfer. The research proposes a novel temperature visualization method that integrates laser-based diagnostic tools and advanced deep learning methods to enable the measurement in boiling fluids in complex geometries. The project hypothesizes that advances in deep learning can reconstruct temperature fields in fluids from sparse measurements and correct visualization artifacts, enabling the visualization of spatiotemporal temperature variations in boiling fluids. If successful, the proposed research can significantly advance the fundamental understanding of the following thermal transport phenomena: (1) The impact of flow-structure interaction on the temperature field, thermal boundary layer, and superheated liquid layer development, (2) The mixing behavior of the thermal boundary layer during vapor evaporation, bubble departure from the surface, and rewetting of the dry spot after microlayer evaporation, and (3) The distribution of local heat transfer coefficients on finned surfaces at different phases of the boiling process. The improved understanding will contribute to the design of more effective finned heat exchangers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.