This paper is NOT THE PUBLISHED VERSION; but the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation below.

This paper is NOT THE PUBLISHED VERSION; but the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in th citation below.

No abstract

Latent thermal energy storage (TES) systems which store energy in a phase change material (PCM) can be used to alleviate disparities in energy supply and demand for a variety of applications from concentrated solar plants to building heating and cooling systems. Furthermore, multiple TES modules containing PCMs with different melt temperatures can be combined in series and parallel configurations to construct a multi-temperature TES assembly to allow for wider flexibility in system optimization. This study demonstrates the feasibility of using machine learning based control methods to operate multi-temperature TES assemblies to ensure both operational reliability and optimization of energy usage. Two different TES assemblies are considered: one with on/off valves controlling flow to individual modules and one with fully modulating valves. Theoretical models are developed to determine the best flow path for the working fluid through each TES assembly for a variety of inlet temperatures and flow rates based on a dual optimization of matching a target outlet temperature and minimizing exergy destruction. Artificial Neural Network (ANN) controllers are then developed for each of the systems to predict the best operating mode based on the inlet temperature and flow rate to the TES assembly. The results demonstrate that an ANN controller can be used to successfully operate a multi-temperature TES assembly and maintain high operational reliability by matching a desired outlet temperature while also improving efficiency by decreasing the fraction of exergy destroyed to total energy transferred.

A multiphase lattice Boltzmann model is used to explore the presence, evolution, and behavior of nanobubbles. The existence and behavior of nanobubbles has been a recent area of interest since the presence of nanobubbles challenges classical nucleation theory which dictates that bubbles below the critical radius should collapse. Nanobubbles have many areas of interest including cleaning of surfaces, nucleate boiling in microchannels, and nucleation on nanostructured materials. Multiphase Lattice Boltzmann methods (LBM) have been demonstrated to be an effective mesoscale approach to modeling multiphase flows and phase-change processes. These methods provide accurate macroscopic results while accounting for microscopic interactions without invoking an extraordinary computational cost. In this study, an LBM is used to model the evolution of nanobubbles with diameters ranging from 5 to 50 nanometers. LBM results are provided for a variety of real physical conditions that are of interest for exploring nanobubble existence within a nanoporous layer. In addition to the single nanobubble analysis, the effects of bubble interaction with smooth surfaces and within nanostructured surfaces are also presented. The results show that the hydrophilic nature of the surfaces is likely the cause of suppression in the onset of nucleate boiling which is often seen in hydrophilic nanoporous layers. The implications of these results on heat transfer applications including multiphase flows and nucleate boiling in roughened nanostructured surfaces are discussed.