[CCoE Notice] PhD Defense

[Knudsen, Rachel W]

NAME: Parham Jafari

COMMITTEE CHAIR: Dr. Hadi Ghasemi

DATE: Tuesday, November 24, 2020

TIME: 9:00 AM

LOCATION: Zoom meeting ID: 955 2581 8556 (Please email for passcode) - (hghasemi at Central.UH.EDU)

TITLE:

On Physics of evaporation through AI-models, Boltzmann transport simulations, and experiments for extreme thermal management of electronics/photonics



Evaporation is a fundamental and core phenomenon in a broad range of disciplines including power generation and refrigeration systems, desalination, electronic/photonic cooling, aviation systems, and even biosciences. Evaporative mass flux is governed by the interfacial state of liquid and vapor phases. For closely similar pressures and mass fluxes of liquid water into its own vapor, discontinuity between interfacial liquid and vapor temperatures in the range of 0.14-28 K is reported. This controversial discontinuity has resulted in an obstacle on understanding and theoretical modeling of evaporation. Here, through study of vapor transport by Boltzmann transport equation solved through Direct Simulation Monte Carlo Method, we demonstrated that the measured discontinuities were strongly affected by boundary conditions on the vapor side of the interface and do not reflect the interfacial state. The temperature discontinuity across the evaporating interface is ≤ 0.1 K for all these studies. To accurately capture the interfacial state, the vapor heat flux should be suppressed.

The trend in miniaturization and enhanced functional performance of integrated circuits and power electronics and photonics has amplified the generated thermal energy in these devices making thermal management a bottleneck for further advancement in these fields. A range of geometries of hierarchical structures are developed and examined to address this challenge. However, the numerous form factors and dimension of hierarchical structures plus cost and time-consuming synthesis and test procedures make it infeasible to explore bountiful variations of hierarchical geometries through experimental methods. Here, we introduce a general Artificial Intelligence (AI) platform to address this challenge and guide discovery of hierarchical structures for extreme thermal management of high-performance photonics/electronics. The AI platform is based on Random Forest (RF) algorithm, a robust AI method, and  was trained using a large collected experimental data set corresponding to thin film evaporation in various forms of hierarchical structures. Four geometrical dimensions of the hierarchical structures and two dimensionless numbers governing heat transfer and fluid dynamics were used as independent variables to predict heat flux in these structures. The trained model's performance was analyzed and showed an excellent prediction of heat flux for all the structures with various working fluids.




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