Doctor of Philosophy / Department of Mechanical and Nuclear Engineering / Amy R. Betz / Boiling is a very effective way of heat transfer due to the latent heat of vaporization. Large amount of heat can be removed as bubbles form and leave the heated surface. Boiling heat transfer has lots of applications both in our daily lives and in the industry. The performance of boiling can be described with two important parameters, i.e. the heat transfer coefficient (HTC) and the critical heat flux (CHF). Enhancing the performance of boiling will greatly increase the efficiency of thermal systems, decrease the size of heat exchangers, and improve the safety of thermal facilities. Boiling heat transfer is an extremely complex process. After over a century of research, the mechanism for the HTC and CHF enhancement is still elusive. Previous research has demonstrated that fluid properties, system pressures, surface properties, and heater properties etc. have huge impact on the performance of boiling. Numerous methods, both active and passive, have been developed to enhance boiling heat transfer. In this work, the effect of pressure was investigated on a plain copper substrate from atmospheric pressure to 45 psig. Boiling heat transfer performance enhancement was then investigated on Teflon© coated copper surfaces, and graphene oxide coated copper surfaces under various system pressures. It was found that both HTC and CHF increases with the system pressure on all three types of surfaces. Enhancement of HTC on the Teflon© coated copper surface is contributed by the decrease in wettability. It is also hypothesized that the enhancement in both HTC and CHF on the graphene oxide coated surface is due to pinning from micro and nanostructures in the graphene oxide coating or non-homogeneous wettability. Condensation and freezing experiments were conducted on engineered surfaces in order to further characterize the pinning effect of non-homogeneous wettability and micro/nano structure of the surface.

In this thesis the major objective is the implementation of the inverse neural network concept in the design of printed lens (transmitarray) antenna. As it is computationally extensive to perform full-wave simulations for entire transmitarray structure and thereafter perform optimization, the idea is to generate a design database assuming that a unit cell of the transmitarray is situated inside a 2D infinite periodic structure. This way we generate a design database of transmission coefficient by varying the unit cell parameters. Since, for the actual design, we need dimensions for each cell on the transmitarray aperture and to do this we need to invert the design database. The major contribution of this thesis is the proposal and the implementation of database inversion methodology namely inverse neural network modelling. We provide the algorithms for carrying out the inversion process as well as provide check results to demonstrate the reliability of the proposed methodology. Finally, we apply this approach to design a transmitarray antenna, and measure its performance.