More Like this

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. 

   more » « less
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. 

   more » « less
3. 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. 

   more » « less
4. Lattice Boltzmann Simulation of The Effects of Nanobubble Morphology and Surface Nanofeatures on Superheat-Driven Nucleation and Bubble Growth

   Silva, A; Carey, VP (July 2025, ASME)

   ABSTRACT Bubble nucleation associated with nucleate boiling at superheated surfaces has typically focused on surfaces with features on the order of microns and bubble embryos with comparable interface radii of curvature. For such surfaces the vapor embryo growth or collapse behavior is consistent with surface tension and wetting forces being confined to the contact line region at the surface. In a pure fluid saturating a superheated nanopororus layer, random density fluctuations can lead to the formation of nanoscale bubble embryos. Said fluctuations increase as the liquid is superheated and can lead to macroscopic nucleation. Entrapment of gas when nanostructured surfaces are flooded with liquid can also result in nanoscale bubble embryos in or near the porous layer. For highly wetted nanostructured surfaces, the fluid-to-surface attractive forces are strong over much of a nanobubble embryo, and the critical bubble size that results in spontaneous bubble growth is affected more strongly by surface forces. A Lattice Boltzmann model (LBM) is used to simulate the time evolution behavior of bubble embryos, with radii ranging from 5 to 15 nanometers, close to or within nanoscale interstitial spaces of a nanostructured surface. Single vapor nanobubbles are seeded in surrounding fluid with varying degrees of contact with solid surfaces to simulate smooth or nanostructured surfaces. The effects of varying adsorption coefficient (which dictates contact angle), varying bubble surface radius of curvature, mean distance of wall nanostructures from the embryo, and varying degrees of enclosure of the embryo by surrounding wall structures are explored. The simulation results indicate that the critical radius is largely impacted by the proximity of nanostructures, demonstrating how the fluid-surface forces affect the stability of a vapor embryo. The results suggest that the hydrophilic nature of the surfaces contributes to the suppression in the onset of nucleate boiling which is often seen in hydrophilic nanoporous layers. The implications of these results on convective and nucleate boiling at and within nanostructured surfaces are also discussed. 

   more » « less
5. MACHINE LEARNING COMBINED IMAGE AND SENSOR DATA ANALYSIS OF TWO-PHASE MORPHOLOGY AND HEAT TRANSFER DATA FOR DROPLET VAPORIZATION ON A SURFACE AT NEAR CRITICAL HEAT FLUX CONDITIONS

   Tchouteng-Njike, U; Silva, Anisa; Carey, VP (July 2025, ASME)

   ABSTRACT For droplet vaporization on a superheated hydrophilic surface, earlier studies have demonstrated that use of machine learning tools to analyze both image information from high-speed video and digital data from sensors can be an effective path to understanding the physics and developing a useful model to predict performance when the surface superheat is at low to moderate levels. For such conditions, the two-phase morphology of the system is usually well-behaved, exhibiting conduction-dominated film evaporation of the spread droplet, or nucleate boiling at active nucleation sites in the liquid film of the spread droplet. At higher surface superheat levels, experiments have shown that the droplet vaporization process becomes chaotic, with the process alternating between rapid vaporization of liquid in contact with the surface and ejection of liquid off the surface by strong vapor recoil forces. For our experiments with water droplets at atmospheric pressure, this regime corresponds to superheat levels ranging from about 35 to 55 deg. C. At the low superheat end of this regime, extremely high mean heat flux levels are achieved, but as superheat further increases, less of the surface stays wetted due to the increasing vapor recoil forces, and heat flux begins to decrease as the boiling process becomes like transition pool boiling with progressively less of the surface in contact with liquid. This exploration of the use of a specialized convolution neural network (CNN) to simultaneously analyze high speed video images and digital data for this high-superheat, near-critical-heat-flux regime of droplet vaporization is of special interest for two reasons. First, this vaporization regime results in high heat flux levels that make it attractive for high heat flux cooling for high-powered electronics. Use of machine learning tools to learn more about the mechanisms of this vaporization regime may open the door to new high flux thermal management technologies. In addition, because of its complexity, the two-phase morphology of the vaporization process in this regime is expected to be a very challenging task for CNN machine learning tools. In this study we conducted deposited water droplet spreading and vaporization experiments that captured digital data input (measured surface superheat, mean heat flux during the vaporization process, wetting contact angle, droplet size, etc.) and images of the droplet vaporization two-phase morphology from high-speed video during each experiment. This paper summarizes our successful development of a specialized hybrid CNN design that is trained using the combination of digital measurements and images obtained in our experiments. This CNN design provides deep insight into correlation between the two-phase morphology and heat transfer performance for this near critical heat flux vaporization regime. It also provides a pathway to a heat transfer performance model that fits the performance data to a high level of agreement. Using data collected from the droplet deposition experiment, this network design has been trained to predict the mean heat flux with a root mean square percent error of only about 2.0% and 8.0% 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 with complex morphology that are best investigated experimentally with a combined use of imaging and digital sensor instrumentation. 

   more » « less