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Title: 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
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
Award ID(s):
2228373
NSF-PAR ID:
10482389
Author(s) / Creator(s):
;
Publisher / Repository:
American Society of Mechanical Engineers
Date Published:
Journal Name:
Proceedings of the ASME 2023 Heat Transfer Summer Conference HT2023
Subject(s) / Keyword(s):
["machine learning","droplet vaporization","onset of nucleation, nanostructured surface coating","nucleate boiling effects"]
Format(s):
Medium: X
Location:
Washington, DC
Sponsoring Org:
National Science Foundation
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