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1. 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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2. Use of A Genetic Algorithm to Model the Interaction of Conduction and Nucleate Boiling Mechanisms During Evaporation of Water Droplets on Superheated ZnO Nanostructured Surfaces, paper HT2023-107422

   Silva, Anisa D.; Carey, Van P. (July 2023, Proceedings of the ASME 2023 Heat Transfer Summer Conference HT2023)

   ABSTRACT At low surface superheat levels, water droplets deposited on ZnO nanostructured surfaces vaporize primarily by conduction transport of heat from the solid heated surface to the liquid-vapor interface. As the superheat is increased beyond the onset of bub- ble nucleation threshold (ONB), an increasing number of active nucleation sites are observed within the evaporating droplet re- ducing the time required to completely evaporate the droplet. There were two primary objectives of this investigation; first, to determine how system parameters dictate when ONB occurs and how its heat transfer enhancement effect increases with superheat. The second was to develop a physics-inspired model equation for the evaporation time of a droplet on a nanostructured surface which accounts for effects of conduction transport in the liquid layer of the droplet and nucleate boiling. A shape factor model for conduction-dominated vaporiza- tion of the droplet was first constructed. A correction factor was introduced to account for deviation of the measured droplet evaporation times from the conduction-dominated model. The correction factor form was postulated using a modified form of the onset of nucleate boiling parameter used in the well-known model analysis developed by Hsu to predict onset of nucleation and active nucleation site range in a thermal boundary layer as- sociated with forced convection boiling. Droplet footprint radii were experimentally observed to be affected by superheat and an additional term was introduced to account for this phenomenon. A term was also introduced to include correlations for boiling to incorporate system properties. This modeling led to an evaporation time equation contain- ing numerical constants dictated by the idealizations from the physical modeling. To develop an improved empirical model equation, these numerical values were taken to be adjustable constants, and a genetic algorithm was used to determine the ad- justable constant values that best fit a data collection spanning wide variations of droplet size, surface apparent contact angle, and superheat level. The best-fit constants match the data to an absolute fractional error of 26%. The model equation developed in this study provides insight into the interaction between con- duction transport and nucleate boiling effects that can arise in droplet vaporization processes. 

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3. 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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4. 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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5. Deep learning predicts boiling heat transfer

   https://doi.org/10.1038/s41598-021-85150-4  

   Suh, Youngjoon; Bostanabad, Ramin; Won, Yoonjin (December 2021, Scientific Reports)

   Abstract Boiling is arguably Nature’s most effective thermal management mechanism that cools submersed matter through bubble-induced advective transport. Central to the boiling process is the development of bubbles. Connecting boiling physics with bubble dynamics is an important, yet daunting challenge because of the intrinsically complex and high dimensional of bubble dynamics. Here, we introduce a data-driven learning framework that correlates high-quality imaging on dynamic bubbles with associated boiling curves. The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features. By training on these features, our model learns physical boiling laws that statistically describe the manner in which bubbles nucleate, coalesce, and depart under boiling conditions, enabling in situ boiling curve prediction with a mean error of 6%. Our framework offers an automated, learning-based, alternative to conventional boiling heat transfer metrology. 

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