Computer vision-assisted investigation of boiling heat transfer on segmented nanowires with vertical wettability
Computer vision-assisted investigation of boiling heat transfer on segmented nanowires with vertical wettability
复制标题
计算机视觉辅助研究具有垂直润湿性的分段纳米线上的沸腾传热
DOI：
10.1039/d2nr02447k
复制
发表时间：
2022
期刊：
影响因子：
6.7
通讯作者：
Won, Yoonjin
中科院分区：
文献类型：
--
作者：
Lee, Jonggyu;Suh, Youngjoon;Kuciej, Max;Simadiris, Peter;Barako, Michael T.;Won, Yoonjin
沸腾效率本质上取决于气泡成核的需要和蒸汽去除的必要性之间的权衡。这些相互竞争的需求的解决方案需要分离气泡活动和液体输送，通常通过表面工程来实现。在这项研究中，我们独立工程师的气泡成核和离开机制，通过设计异质和分段的纳米线与双重润湿性，目的是推动结构增强沸腾传热性能的极限。分离液体和蒸气路径的演示优于最先进的分层纳米线，特别是在低热通量状态下，同时在高热通量下保持相同的性能。基于深度学习的计算机视觉框架实现了隐藏大数据的自主管理和提取，沿着数字化气泡。材料设计、深度学习技术和数据驱动方法的综合努力揭示了蒸汽/液体路径、气泡统计和相变性能之间的机械关系。
The boiling efficacy is intrinsically tethered to trade-offs between the desire for bubble nucleation and necessity of vapor removal. The solution to these competing demands requires the separation of bubble activity and liquid delivery, often achieved through surface engineering. In this study, we independently engineer bubble nucleation and departure mechanisms through the design of heterogeneous and segmented nanowires with dual wettability with the aim of pushing the limit of structure-enhanced boiling heat transfer performances. The demonstration of separating liquid and vapor pathways outperforms state-of-the-art hierarchical nanowires, in particular, at low heat flux regimes while maintaining equal performances at high heat fluxes. A deep-learning based computer vision framework realized the autonomous curation and extraction of hidden big data along with digitalized bubbles. The combined efforts of materials design, deep learning techniques, and data-driven approach shed light on the mechanistic relationship between vapor/liquid pathways, bubble statistics, and phase change performance.