Investigation of Nucleate Boiling Mechanisms using 3D Transient Temperature Mapping

使用 3D 瞬态温度图研究泡核沸腾机制

• 批准号: 1917272
• 负责人: Myeongsub Kim
• 金额: $ 30.68万
• 依托单位: Florida Atlantic University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917272&HistoricalAwards=false
• 关键词: Investigation; Nucleate; Boiling; Mechanisms; using

The rapid advancement of nanotechnology and microelectronics poses significant challenges to the thermal management of extreme heat loads discharged from tightly confined areas in electrical systems. Harnessing boiling heat transfer associated with bubble growth is perhaps one of the most efficient cooling methodologies for electrical systems due to the large amount of heat removal during the phase change from water to vapor. Despite significant enhancements in heat removal rates, numerous questions remain regarding the fundamentals of bubble growth mechanisms, a major source of enhanced heat dissipation. The project goal is to develop a mechanistic model for bubble growth that lessens the risk of failure in trial-and-error tests for heat transfer enhancement. This will, in turn, provide a systematic design route for next-generation cooling systems while saving time and money. This project integrates research activities with educational goals such as offering underrepresented minority and female graduate and undergraduate students with hands-on research experiences. The research objective of this project is to accurately measure local liquid temperature distributions surrounding a growing bubble that help better explain the heat and mass transfer to bubble growth. Local fluid temperatures in the microlayer are interrogated by total internal reflection thermometry while fluid temperatures in the thermal boundary layer near the liquid-vapor interface are measured by dual-tracer laser-induced fluorescence thermometry. To capture transient temperature distributions, both techniques are combined with high-speed imaging. The transient fluid temperature data are used to quantify time-resolved heat fluxes contributing to mass transfer near the growing bubble. Comprehensive 3D temperature information is also used to validate the existing theoretical and experimental thermal transport models. This project is scientifically significant in that it illuminates the dominant heat transfer mode for fast bubble growth and therefore provides reliable methodologies to engineer surface and fluid properties for enhanced heat transfer without a trial-and-error process.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.

纳米技术和微电子技术的快速发展对电气系统中严格限制区域排放的极端热负荷的热管理提出了重大挑战。利用与气泡生长相关的沸腾换热可能是电气系统最有效的冷却方法之一，因为在从水到蒸汽的相变过程中会有大量的热量转移。尽管散热率有了显著的提高，但关于气泡生长机制的基本原理仍然存在许多问题，气泡生长机制是增强散热的主要来源。该项目的目标是开发一种气泡生长的机械模型，以降低在强化传热的反复试验中失败的风险。这反过来将为下一代冷却系统提供一条系统的设计路线，同时节省时间和金钱。该项目将研究活动与教育目标结合起来，例如为代表不足的少数族裔和女性研究生和本科生提供实践研究经验。本项目的研究目标是准确测量不断增长的气泡周围的局部液体温度分布，以帮助更好地解释气泡生长过程中的热量和质量传递。微层内的局部流体温度用全内反射测温法测量，而气液界面附近热边界层的流体温度用双示踪激光诱导荧光测温法测量。为了捕捉瞬时温度分布，这两种技术都与高速成像相结合。瞬变流体温度数据被用来量化时间分辨的热通量，这些热通量有助于在不断增长的气泡附近进行传质。利用综合的三维温度信息对现有的理论和实验热传输模型进行了验证。该项目具有重要的科学意义，因为它阐明了气泡快速生长的主要换热模式，因此提供了可靠的方法来设计表面和流体属性，以增强换热，而无需反复试验。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。

项目成果

A review on correlations of bubble growth mechanisms and bubble dynamics parameters in nucleate boiling

• DOI: 10.1007/s10973-021-10876-2
• 发表时间: 2021-07
• 期刊: Journal of Thermal Analysis and Calorimetry
• 影响因子: 4.4
• 作者: Mahyar Ghazivini;Mazen Hafez;Abhishek Ratanpara;Myeongsub Kim

Experimental Study of Bubble Growth on Novel Fin Structures during Pool Boiling

• DOI: 10.1016/j.ijmultiphaseflow.2023.104568
• 发表时间: 2023-07
• 期刊: International Journal of Multiphase Flow
• 影响因子: 3.8
• 作者: Mahyar Ghazvini;Mazen Hafez;P. Mandin;M. Kim

Combination of baffling technique and high-thermal conductivity fluids to enhance the overall performances of solar channels

• DOI: 10.1007/s00366-020-01165-x
• 发表时间: 2020-09
• 期刊: Engineering with Computers
• 影响因子: 8.7
• 作者: Y. Menni;Mahyar Ghazvini;H. Ameur;Myeongsub Kim;M. Ahmadi;M. Sharifpur

Optimization of MLP neural network for modeling flow boiling performance of Al2O3/water nanofluids in a horizontal tube

• DOI: 10.1016/j.enganabound.2022.09.034
• 发表时间: 2022-12
• 期刊: Engineering Analysis with Boundary Elements
• 影响因子: 3.3
• 作者: Mahyar Ghazvini;S. Varedi-Koulaei;M. Ahmadi;Myeongsub Kim