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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. 

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.