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

该项目利用深度学习辅助实验技术来可视化地研究沸腾流体中的温度变化。沸腾传热在航空、太空探索、电动汽车、工业用热等各个行业中起着举足轻重的作用。尽管它的重要性，基本问题仍然存在关于沸腾传热，包括理解独特的流动模式，温度分布，气泡大小和轨迹。这些挑战来自建模和可视化温度的困难。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。该项目的成果预计将通过建立复杂传热系统的知识和计量学而与工业应用相关。一个特别感兴趣的领域是提高热交换器的能量效率。数据中心的冷却（使用热交换器）约占美国所有电力生产的1%，每年造成340亿美元的成本和1.37亿公吨的二氧化碳。因此，探索先进的温度计量和分析以提高热交换器性能的新机会将对能源产生巨大影响。此外，该项目还通过三项任务为教育做出贡献：（1）为机器学习建模创建教育视频和练习;（2）为K-12和本科生开发结构化的学习活动;（3）与英特尔公司合作，激励非学术研究环境中的学生。对于沸腾流体在翅片表面上的温度场的时空变化规律还缺乏认识，翅片表面是一种复杂的基本传热方式。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，可以测量复杂几何形状的沸腾流体。该项目假设，深度学习的进步可以从稀疏测量和正确的可视化伪影中重建流体中的温度场，从而实现沸腾流体中时空温度变化的可视化。如果成功，拟议的研究可以显着推进对以下热传输现象的基本理解：（1）流动-结构相互作用对温度场、热边界层和过热液体层发展的影响，（2）蒸汽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润湿，（3）沸腾过程不同阶段翅片表面局部换热系数的分布。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.