Date of Award

Summer 8-27-2021

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Mechanical and Aerospace Engineering

Advisor(s)

Maroo, Shalabh

Keywords

Confined Nanochannels, Heat Transfer at Nanoscale, Machine Learning, Molecular Dynamics, Thermal Management, Vapor Bubble Nucleation

Subject Categories

Atomic, Molecular and Optical Physics | Engineering | Mechanical Engineering | Nanoscience and Nanotechnology | Physical Sciences and Mathematics | Physics

Abstract

Heat transfer from a solid surface to fluid occurs in numerous applications, such as boilers, refrigerators, power electronics, etc., and include various underlying phenomena of thin-film evaporation, boiling, condensation, surface wetting, and droplet evaporation. Several studies show that heat transfer can be enhanced significantly by incorporating micro and nanoscale features onto surfaces. Phase change heat transfer plays an important role in dissipating energy from hot surfaces and distributing it to bulk liquid due to high latent heat of liquids. Boiling, a phase change heat transfer phenomenon, happens when the surface is heated to a temperature above the saturation temperature of the liquid. Therefore, it is important to understand bubble formation, its onset, and behavior over time to advance thermal management applications utilizing nanoscale characteristics. This work aims to investigate bubble nucleation and growth on heated surfaces and understand heat transfer at liquid-vapor/liquid-solid interfaces at the molecular level towards gaining fundamental knowledge of the boiling process in bulk liquid as well as nanoscale confinement. To mimic the surface heating of liquid, a surface-to-fluid heat transfer model is implemented in open-source MD software LAMMPS. The implementation is first validated for transient as well as steady-state conditions against a conventional conduction problem. Since vapor bubble requires a study of the liquid-vapor interface, the implemented model is used to investigate the thermodynamic properties of interfaces at various temperatures. At this quasi-steady state, accommodation coefficients of argon are also determined by the Hertz-Knudsen-Schrage relation for condensation and evaporation interfaces. After understanding the liquid-vapor interface, the onset of vapor bubble formation in liquid argon on the platinum surface is investigated using molecular dynamics simulations. Heterogeneous bubble nucleation is initiated on a partially heated platinum surface submerged in a pool of liquid argon. An analytical model for a critical radius of nucleation is derived using the vapor bubble behavior and thermodynamic properties of vapor and liquid. The study is extended for nano-confinement bubble nucleation and the effects of surface hydrophilicity are investigated. The results show that vapor bubble needs more energy to nucleate in confinement, hence making them a better option for thermal management. As traditional numerical methods are computationally expensive with simulations time ranging from several hours to several days, machine learning models are trained to measure heated surface temperature. A linear regression model and shallow neural networks are developed on a 2D temperature distribution in a nanochannel. The pre-trained model can be helpful in case direct measurement is not possible, like nucleation temperature on thick non-metallic surfaces.

Access

Open Access

Download

Share

COinS