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1. Exploration of the Effects of Nanoscale Surface Morphology Variations on Onset of Bubble Nucleation in Water Droplets Impinging and Boiling on Nanostructured Surfaces

   Silva, Anisa D; Carey, Van P (July 2024, American Society of Mechanical Engineers)

   Nanostructured hydrophilic surfaces can enhance boiling processes due to the liquid wicking effect of the small surface structures, but consistently uniform nanoscale interstitial spaces would provide very few heterogeneous nucleation sites, which would require high superheat to activate in, for example, liquid water. Experiments indicate that surfaces of this type initiate onset of nucleate boiling at relatively low superheat levels, implying that larger-than-average interstitial spaces exist, apparently as a consequence of larger micron-scale variations of the surface structure or surface chemistry (wetting) resulting from the fabrication process. The investigation summarized here explores the potential correlation between nanostructured surface morphology variations and onset of nucleation. A zinc oxide nanostructured coating was fabricated on a copper substrate for experiments and analysis in this study. The coated surface was subjected to water droplet deposition tests to evaluate wicking and contact angle, followed by vaporization tests at varying surface superheat levels, and extensive electron microscopy imaging of the surface. The results of the vaporization experiments determined the variation of mean heat flux to the droplet as a function of superheat, and high-speed videos documented the superheat at which onset of nucleate boiling (ONB) occurs and variation of nucleation site density with superheat. Image analysis of the electron microscopy images were used to assess the variability of pore size and surface complexity (entropy) over the surface. By determining macroscope bubble nucleation and boiling performance from measured data and high-speed video records for these surfaces, and simultaneously analyzing the morphology of that surface at the micro/nano scale, our data demonstrates the correlation between surface morphology variations and ONB and nucleate boiling active site density. Specifically, our results indicate that increased irregularities in the surface morphology correspond to enhanced probability of nucleation onset and an increase in active nucleation site density as superheat increases. Our data indicates the range of irregularity number density values (number per square millimeter) and the imperfection features that give rise to consistent low superheat ONB (∼ 15◦𝐶), leads to a robust increase in active site density during nucleate boiling as super heat increases. This information can help guide development of enhanced boiling surfaces by providing insight into the frequency of nanosurface morphology variations, per square millimeter, that enhance nucleation onset while also providing enhanced wicking and low contact angle over most of the surface. The implication of these results for design of different types of enhanced boiling surfaces is also discussed. 

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2. Exploration of the Effects of Nanoscale Surface Morphology Variations on Onset of Bubble Nucleation in Water Droplets Impinging and Boiling on Nanostructured Surfaces; paper SHTC2024-131037

   Silva, Anisa D; Carey, Van P (July 2024, American Society of Mechanical Engineers)

   Nanostructured hydrophilic surfaces can enhance boiling processes due to the liquid wicking effect of the small surface structures, but consistently uniform nanoscale interstitial spaces would provide very few heterogeneous nucleation sites, which would require high superheat to activate in, for example, liquid water. Experiments indicate that surfaces of this type initiate onset of nucleate boiling at relatively low superheat levels, implying that larger-than-average interstitial spaces exist, apparently as a consequence of larger micron-scale variations of the surface structure or surface chemistry (wetting) resulting from the fabrication process. The investigation summarized here explores the potential correlation between nanostructured surface morphology variations and onset of nucleation. A zinc oxide nanostructured coating was fabricated on a copper substrate for experiments and analysis in this study. The coated surface was subjected to water droplet deposition tests to evaluate wicking and contact angle, followed by vaporization tests at varying surface superheat levels, and extensive electron microscopy imaging of the surface. The results of the vaporization experiments deter- mined the variation of mean heat flux to the droplet as a function of superheat, and high-speed videos documented the superheat at which onset of nucleate boiling (ONB) occurs and variation of nucleation site density with superheat. Image analysis of the electron microscopy images were used to assess the variability of pore size and surface complexity (entropy) over the surface. By determining macroscope bubble nucleation and boiling performance from measured data and high-speed video records for these surfaces, and simultaneously analyzing the morphology of that surface at the micro/nano scale, our data demonstrates the correlation between surface morphology variations and ONB and nucleate boiling active site density. Specifically, our results indicate that increased irregularities in the surface morphology correspond to enhanced probability of nucleation onset and an increase in active nucleation site density as superheat increases. Our data indicates the range of irregularity number density val- ues (number per square millimeter) and the imperfection features that give rise to consistent low superheat ONB (∼ 15◦𝐶), leads to a robust increase in active site density during nucleate boiling as super heat increases. This information can help guide development of enhanced boiling surfaces by providing insight into the frequency of nanosurface morphology variations, per square millimeter, that enhance nucleation onset while also providing enhanced wicking and low contact angle over most of the surface. The implication of these results for design of different types of enhanced boiling surfaces is also discussed. 

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3. A Convolution Neural Network Design for Combined Image and Sensor Data Analysis to Determine Droplet Vaporization Regime and Heat Transfer Performance

   Tchouteng_Njike, Ursan; Silva, Anisa; Carey, Van P (July 2024, American Society of Mechanical Engineers)

   Combining high-speed video cameras and optimal measurement techniques with digital sensors controlled by a data acquisition system can yield a combination of experimental tools to explore boiling process thermophysics and heat transfer mechanisms. Imaging can provide qualitative and quantitative information that complements data provided by temperature, pressure, and more sensors. This paper summarizes the results of an exploration of machine learning strategies to optimally combine and analyze boiling process images and digital sensor information from experiments. We specifically sought a convolution neural network to analyze the vaporization of deposited water droplets on superheated surfaces that may have varying degrees of nucleate boiling effects. Through experimentation, we found that a hybrid parallel-series convolution/neuron neural network design worked very effectively. The network could extract the regime of droplet vaporization (conduction driven only, conduction plus nucleate boiling, or explosive boiling), the liquid morphology, and could predict the vaporization regime, the wall superheat, and mean heat transfer rate as a function of image input and operating system parameters. Using data collected from the droplet deposition experiment, this network design has been trained to predict the mean heat transfer rate with a root mean square percent error (RMSPE) of only 3.3% and 7.2% on a training and testing dataset respectively. The hybrid network developed in this research appears to be a promising strategy for analyzing experimental data for physical systems that are best investigated experimentally with a combined use of imaging and digital sensor instrumentation. 

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4. A Convolution Neural Network Design for Combined Image and Sensor Data Analysis to Determine Droplet Vaporization Regime and Heat Transfer Performance; paper SHTC2024- 131218

   Tchouteng_Njike, Ursan; Silva, Anisa; Carey, Van P (July 2024, American Society of Mechanical Engineers)

   Combining high-speed video cameras and optimal measurement techniques with digital sensors controlled by a data acquisition system can yield a combination of experimental tools to explore boiling process thermophysics and heat transfer mechanisms. Imaging can provide qualitative and quantitative information that complements data provided by temperature, pressure, and more sensors. This paper summarizes the results of an exploration of machine learning strategies to optimally combine and analyze boiling process images and digital sensor information from experiments. We specifically sought a convolution neural network to analyze the vaporization of deposited water droplets on superheated surfaces that may have varying degrees of nucleate boiling effects. Through experimentation, we found that a hybrid parallel-series convolution/neuron neural network design worked very effectively. The network could extract the regime of droplet vaporization (conduction driven only, conduction plus nucleate boiling, or explosive boiling), the liquid morphology, and could predict the vaporization regime, the wall superheat, and mean heat transfer rate as a function of image input and operating system parameters. Using data collected from the droplet deposition experiment, this network design has been trained to predict the mean heat transfer rate with a root mean square percent error (RMSPE) of only 3.3% and 7.2% on a training and testing dataset respectively. The hybrid network developed in this research appears to be a promising strategy for analyzing experimental data for physical systems that are best investigated experimentally with a combined use of imaging and digital sensor instrumentation. 

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5. Experimental Investigation of Condensation and Freezing Phenomenon on Hydrophilic and Hydrophobic Titanium Nanopillared Glass Surfaces

   https://doi.org/10.1080/01457632.2019.1707401  

   Rejaul Haque, Mohammad; Zhu, Chen; Qu, Chuang; Kinzel, Edward C.; Rachel Betz, Amy (January 2020, Heat Transfer Engineering)

   Atmospheric condensation is very important for multiple practical applications such as heat transfer, thermal management, aerospace, and condensate harvesting. Water droplets heterogeneously nucleate on the surfaces when the temperature is below the dew point temperature. The nucleation energy barrier for a condensed droplet varies significantly with the humidity content in the operating environment. The freezing of this condensate is also dependent on the operating conditions and surface properties. This article presents an experimental study of condensation and freezing from humid air with the objective of understanding how the surface morphology and chemistry determines the droplet shape and wetting state. Hexagonal close-packed arrays of titanium (Ti) pillars are patterned using microsphere photolithography (MPL). The Ti nanostructured surface was tested with and without a Teflon© coating to reveal the condensate harvesting, passive freezing, and dropwise condensation applications, respectively. Condensation and freezing tests were conducted in the presence of non-condensable gases (air) with different relative humidity (RH) levels to control the nucleation site density. The experiments showed that droplet growth occurs in the following stages: initial nucleation, direct growth, and coalescence events. By pinning droplets, coalescence is suppressed for the Ti nanopillared surface altering the size distribution of droplets and significantly accelerating the freezing process. 

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