Abstract

Phase change characteristics of thin film liquid argon subjected to ultrafast boundary heating for different liquid film thicknesses (3 nm ~ 6 nm), boundary heating rates (8 × 109 K/s ~ 320 × 109 K/s) for different surface wetting conditions are main objectives of the present study. Molecular dynamics (MD) simulation has been conducted involving a three-phase domain where liquid and vapor argon (Ar) atoms are placed over the solid platinum-like (Pt) surface. Depending on the combination of boundary heating rate and liquid film thickness, two types of phase change phenomena have been observed namely-diffusive evaporation and explosive boiling. The variations in the system temperature, net evaporation number and wall heat flux normal to the surface over time are closely investigated to explicate the evolution of thin film phase change characteristics. Besides, to get a better understanding of phase change phenomena of thin film liquid, the time-averaged wall heat flux (qavg) obtained from the MD simulation has been compared with classical thermodynamics prediction. The thermodynamic heat flux (qtherm) values are in excellent agreement with the time-averaged wall heat flux (qavg) for diffusive evaporation cases while they differ significantly for explosive boiling cases. A comparative study has been performed on the estimation of cumulative energy density in the liquid film prior to the explosive boiling both from macroscopic as well as MD viewpoints based on simplified control volume approach. The cumulative energy density within the liquid film as obtained from macroscopic viewpoint reasonably matches with that obtained in MD approach for hydrophilic and super-hydrophilic surfaces. Interestingly, for all explosive boiling cases, accumulated energy density at the boiling explosion assumes a mean value with 95% confidence level within 5.6% of the mean, which refers to a critical condition in context to energy content of the liquid film in atomistic approach which is in agreement with other macroscopic model prediction.

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