Microscopic Understanding of Two-Phase Heat Transfer on Engineered Surfaces
Authors:   , Rokoni Arif , Sun Ying 
Published: November 2022

Two-phase heat transfer is crucial for thermal management of electronic devices, power generation, refrigeration, and water desalination. The use of engineered surfaces can lead to significant improvement in heat dissipation capabilities in pool boiling applications due to the increased nucleation sites, elongated contact lines, increased bubble departure frequency, and microlayer evaporation via enhanced wicking, where the microlayer evaporation is the most dominant mechanism. Hierarchical surfaces demonstrate both significant and limited improvements in heat dissipation over micro-structured surfaces, where the microscopic understanding of wicking and thin-film evaporation is crucial. Nanostructures in hierarchical surfaces can achieve very large heat dissipation rates due to high capillary pressure, where the kinetic resistance across the Knudsen layer of the evaporating interface dictates the evaporation rates. The validity of using classical relations to estimate evaporation rate across the evaporating interface is questioned in recent experimental studies, that requires further investigations using atomic-scale study of evaporation across the interface. In addition, the complex nature of bubble dynamics during pool boiling on flat and engineered surfaces lead to several theoretical and experimental correlations but are substantially different from each other. With the advancement of computational capabilities and artificial intelligence (AI), data-driven deep learning (DL) approach has been heralded as an alternative to the conventional physics-based approach. In the present study, mathematical modeling, wicking, thin-film evaporation experiments, laser interferometry, and AI are utilized to address the unresolved issues mentioned above in two-phase heat transfer on engineered surfaces. First, the significance of micropatterns in wicking enhancement on hierarchical surfaces is demonstrated using microscopic in-house wicking experiments. A mathematical model to estimate the wicking performance for hierarchical surfaces is developed and extended to estimate the enhancement of thin-film evaporation over bare micropillar surfaces. The in-house wicking and thin-film evaporation experimental results are found to be in good agreement with the proposed model. Secondly, to verify the classical relations of evaporation across the liquid-vapor (L-V) interface, non-equilibrium molecular dynamics simulations are performed. The temperature jump and profile calculated across an evaporating L-V interface at various heat fluxes are found in good agreement with the classical theory predictions. To validate the simulation findings from thermodynamics point of view, atomistic interfacial entropy generation rates are calculated and found in qualitative agreement with the predictions from the non-equilibrium thermodynamics. Finally, to better understand the chaotic nature of bubble dynamics in pool boiling, principal component analysis (PCA) is used to extract dominant low dimensional features from pool boiling experimental images of bubble morphologies. The new physical descriptors, dominant frequency, and its amplitude, extracted from the present unsupervised machine learning are qualitatively compared to the bubble count and size extracted from a supervised deep-learning algorithm. This novel reduced-order analysis appears to be highly robust over multiple datasets and heater surfaces.