Analysis and design of multi-phase heat transfer systems in building HVAC and power plant applications utilizing experimental, numerical and machine learning methods

Phase change heat transfer processes can be leveraged to develop new energy technologies to combat climate change and decarbonize energy systems. This research explores the enhancement of such processes for applications in building HVAC systems and concentrated solar power plants./nNanostructured, superhydrophilic surfaces can be used to enhance two-phase heat transfer processes including spray cooling and pool boiling. Zinc oxide nanopillars are grown on metallic surfaces resulting in a nanoporous layer characterized by ultra-low apparent contact angles and nanoscale porosity leading to enhanced surface wetting, improved nucleate boiling heat transfer, and a delay the onset of the critical heat flux condition. This work explores the spreading behavior of droplets on ZnO nanostructured surfaces in the context of surface wetting (contact angle) and wickability (flow via capillary pressure through the nanoporous layer) to identify the driving forces for droplet impingement. This experimental and numerical study demonstrates that droplets which are touched to the nanoporous surface are driven by wicking while droplets impinging on the surface with non-zero velocity are driven by inertial forces and the ultra-low contact angle. In addition to droplet spreading, nucleation behavior is also explored in this work using Multiphase Lattice Boltzmann Methods to model nanobubble behavior within the nanoporous layer. The results demonstrate that superhydrophilic nanopillars which are too closely packed can suppress nucleation, thus explaining the previously observed phenomenon of a delay in the onset of nucleate boiling on nanostructured surfaces./nLatent thermal energy storage (TES) systems which store energy in a phase change material (PCM) can be used to alleviate disparities in renewable energy supply and demand. In this work, performance characterization experiments were performed for PCM TES modules to construct an experimental database for validation of TES component models. A limitation of such single module PCM systems is the low conductivity of the PCMs. Although performance can be enhanced by using multiple PCMs arranged in a cascaded configuration to improve storage utilization, increase energy and exergy efficiencies, and increase rates for charging and discharging, the benefits of cascaded systems currently rely on precise optimization of design parameters. In this study, a new type of multiple PCM system is proposed called a multi-temperature, multi-module (MTMM) thermal energy storage ensemble which consists of multiple TES modules containing PCMs with different melt temperatures combined in series and parallel configurations. The system uses an Artificial Neural Network (ANN) to optimize control to meet a target outlet temperature and minimize the instantaneous rate of exergy destruction. The results demonstrate that the MTMM ensemble with ANN is able to achieve the exergy efficiency of a cascaded PCM system while also maintaining the flexibility needed to respond to dynamically changing operating conditions.

Advisor: Carey, Van P.