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.

纳米技术和微电子学的快速发展对从电气系统中紧密限制区域排放的极端热量的热量管理提出了重大挑战。利用与气泡生长相关的沸腾传热可能是电气系统最有效的冷却方法之一，因为从水到蒸气的相变量中大量去除热量。尽管排放率显着提高，但关于气泡生长机制的基本原理仍然存在许多问题，这是增强散热的主要来源。项目目标是开发一个机械模型，以降低试验和错误测试的传热增强测试的失败风险。反过来，这将为下一代冷却系统提供系统的设计路线，同时节省时间和金钱。该项目将研究活动与教育目标相结合，例如提供代表性不足的少数民族，女性研究生和本科生，并具有动手研究经验。该项目的研究目标是准确测量围绕生长气泡的局部液体温度分布，以帮助更好地解释热量和传质的气泡生长。微层中的局部流体温度通过总内部反射温度法询问，而液态蒸气界面附近的热边界层的流体温度通过双迹线激光激光诱导的荧光温度测量测量。为了捕获瞬态温度分布，两种技术都与高速成像结合使用。瞬态流体温度数据用于量化有助于大量气泡附近传质的时间分辨热通量。全面的3D温度信息还用于验证现有的理论和实验热传输模型。该项目在科学上具有重要意义，因为它阐明了快速气泡增长的主要传热模式，因此为工程师的表面和流体特性提供了可靠的方法，以增强传热，而无需进行反复试验。这一奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被视为值得的支持。

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