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

职业：使用深度学习增强激光诱导荧光增强翅片表面沸腾流体的温度可视化

• 批准号: 2337973
• 负责人: Beomjin Kwon
• 金额: $ 54.06万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-15 至 2029-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337973&HistoricalAwards=false
• 关键词: CAREER; Enhancing; Temperature; Visualization; Boiling

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.

该项目利用深度学习辅助实验技术来直观地研究沸腾流体的温度变化。沸腾传热在航空、太空探索、电动汽车、工业热等各个行业中发挥着举足轻重的作用。尽管其意义重大，但有关沸腾传热的基本问题仍然存在，包括对独特流动模式、温度分布、气泡尺寸和轨迹的理解。这些挑战源于温度建模和可视化方面的困难。了解温度分布和驱动过程对于下一代热管理系统的开发至关重要。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与工业应用相关。人们感兴趣的一个具体领域是提高热交换器的能源效率。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 亿美元的成本和 1.37 亿吨二氧化碳。因此，探索先进温度计量和分析的新机会以改善换热器性能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。目前，对翅片表面沸腾流体的温度变化的了解还很有限。人们对翅片表面沸腾流体温度场的时空变化缺乏了解，它代表了一种复杂的传热基本模式。该研究提出了一种新颖的温度可视化方法，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步可以通过稀疏测量重建流体中的温度场并纠正可视化伪影，从而实现沸腾流体中时空温度变化的可视化。如果成功，所提出的研究可以显着推进对以下热传输现象的基本理解：（1）流-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）混合行为蒸汽蒸发过程中热边界层的变化、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传热系数的分布。加深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。