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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. 

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. 

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. 

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. 

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. 

Abstract This paper presents an exergy-based sustainability analysis of manufacturing roof tiles from plastic waste in Uganda. This work focuses specifically on the developing country context and on utilizing waste material. A summary of the current Ugandan plastic waste situation, environmental and health issues associated with plastic waste, current means of recycling plastic waste into new products, and an analysis of the Ugandan roofing market is presented. The total exergy consumed to produce one batch of 75 tiles is over 240 MJ, the potentially recoverable exergy is nearly 17 MJ (8% of consumed exergy), and the realistic recoverable exergy is over 6.4 MJ (nearly 3% of consumed exergy). Recycling plastic waste into roof tiles saves a net 188 kg of CO2 from entering the atmosphere per batch when compared with open burning. If all of Kampala’s plastic waste was converted to roofing tiles, nearly 560 tonnes of CO2 could be saved per year. 

Abstract This paper presents an exergy-based sustainability analysis of manufacturing roof tiles from plastic waste in Uganda. Exergy analyses measure the sustainability of industrial processes. This work focuses specifically on the developing country context and on utilizing waste material. A summary of the current plastic waste situation in Uganda, the environmental and health issues associated with plastic waste, current means of recycling plastic waste into new products, and an analysis of the Ugandan roofing market are presented. The motivation for this study is to examine the resources utilized to improve overall exergy efficiency, reduce production costs, and reduce negative environmental impacts. The company, Resintile, is the only manufacturer of roof tiles from plastic waste in Uganda. Their tiles comprised mainly of sand and plastic waste are manufactured in an industrialized process involving drying, extrusion, and pressing. The exergy consumed at each stage including transportation is presented. The extruder consumes the majority of the exergy, but wrapping insulation around the barrel could save over 3 MJ, and a heat engine could provide over 7.5 MJ of usable exergy. The total exergy consumed to produce one batch of seventy-five tiles is over 122 MJ, the potentially recoverable exergy is over 5 MJ (4.3% of consumed exergy), and the realistic recoverable exergy is nearly 10.7 MJ (8.7% of consumed exergy). The realistic can be greater than the potential by adding a heat engine to the sand drying process to generate usable exergy rather than merely recover consumed exergy. Resintile’s plastic roof tiles save a net 86.3 kg of CO2 from entering the atmosphere per batch of tiles and adoption of the suggested improvements to the manufacturing process would save an additional 3.8 kg of CO2 per batch.