Machine learning enabled condensation heat transfer measurement
Machine learning enabled condensation heat transfer measurement
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DOI：
10.1016/j.ijheatmasstransfer.2022.123016
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发表时间：
2022
影响因子：
5.2
通讯作者：
Siavash Khodakarami;Kazi Fazle Rabbi;Youngjoon Suh;Y. Won;N. Miljkovic
中科院分区：
文献类型：
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作者：
Siavash Khodakarami;Kazi Fazle Rabbi;Youngjoon Suh;Y. Won;N. Miljkovic
测量冷凝传热及其相关的传热系数并不简单。严格的测量需要仔细的实验设计和权衡研究，以正确选择传感器类型、样品几何形状和尺寸、冷却液和流速、操作条件、工作流体纯度、吹扫方法和测量协议。传统的基于管的冷凝热传递测量通过测量入口和出口本体冷却剂温度来量化单相冷却剂流的焓的变化。与这种经典和完善的实验方法相关的不确定性很高。高不确定性源于与冷凝过程相关联的高特征传热系数或热通量，使得外管侧上的热阻通常与内部单相冷却剂对流传热热阻处于相同的数量级。即使非常小心并使用不确定性低的极其精确的传感器，热通量和传热系数的相对不确定性也可能在20%至100%的范围内。在这里，我们利用机器学习（ML）开发一个光学可视化方法的滴状冷凝传热特性。使用最先进的智能视觉，我们展示了一种以前从未探索过的方法，用于表征冷凝液滴脱落频率，液滴脱落尺寸和热通量，而无需高速成像。我们通过对厚度为500 nm、1 μm和5 μm的Parylene C涂层光滑铜管样品进行严格的蒸汽冷凝测量来验证我们的技术。我们验证了我们的ML预测与同时获得的数据使用的自定义和完善的冷凝室的的冷凝变化的方法。与传统的传热测量方法相比，我们的ML方法的不确定性是恒定的（± 10%），不随热通量而变化。最后，我们展示了我们的ML测量技术的关键优势，定制的管具有轴向变化的表面特性，导致不同的局部传热系数。我们的ML热传递测量方法能够高保真表征相变热通量，降低相对测量不确定性，解决局部效应，并消除对样品温度测量的需求。
Measuring condensation heat transfer and its associated heat transfer coefficient is not trivial. Rigorous measurements require careful experimental design and tradeoff studies to properly select sensor type, sample geometry and size, coolant fluid and flow rate, operating conditions, working fluid purity, purge methodology, and measurement protocol. Conventional tube-based condensation heat transfer measurements quantify the change in the enthalpy of a single-phase coolant flow via measurement of the inlet and outlet bulk coolant temperatures. The uncertainties associated with this classical and well-established experimental method are high. The high uncertainty stems from the high characteristic heat transfer coefficient or heat flux associated with the condensation process, making the thermal resistance on the external tube side typically on the same order of magnitude as the internal single-phase coolant convective heat transfer thermal resistance. Even when taking the utmost care and using extremely accurate sensors having low uncertainty, the relative uncertainties of heat flux and heat transfer coefficient can be in the range of 20% to 100%. Here, we take advantage of machine learning (ML) to develop an optical visualization method for dropwise condensation heat transfer characterization. Using state-of-the-art intelligent vision, we demonstrate a previously-unexplored method for characterizing the condensate droplet shedding frequency, droplet shedding size, and heat flux without the need for high-speed imaging. We verify our technique by conducting rigorous steam condensation measurements on Parylene C coated smooth copper tube samples having 500 nm, 1 μm, and 5 μm Parylene C thicknesses. We validate our ML predictions with data obtained simultaneously using the enthalpy-change method on a custom and well-established condensation chamber. In contrast to conventional heat transfer measurement methods, the uncertainty of our ML method is constant (∼10%) and does not vary with heat flux. We finally demonstrate the key advantage of our ML measurement technique on a custom-made tube having axially varying surface properties resulting in differing local heat transfer coefficient. Our ML heat transfer measurement method enables the high fidelity characterization of phase change heat flux, reduction in relative measurement uncertainty, resolution of local effects, and elimination of the need for temperature measurement across samples.