Computational Fluid Dynamics (CFD) and predictive models are presented in this dissertation that illustrates the detailed electrical characteristics, and the current-voltage (i-v) relationship throughout the preheating process of premixed methane-oxygen (CH4-O2) oxyfuel cutting flame subject to electric bias voltages. As such, the equations describing combustion, electrochemical transport for charged species, and potential are solved through a commercially available finite-volume CFD code. The reactions of the methane-oxygen (CH4 – O2) flame were combined with the GRI 3.0 mechanism and a 25-species reduced mechanism, respectively, and additional ionization reactions that generate three chemi-ions, H3O+, HCO+, and e– , to describe the chemistry of ions in flames. The electrical characteristics such as ion migrations and ion distributions are investigated for a range of electric potential, V ∈ [−10V, +10V ]. Since the physical flame is comprised of twelve Bunsen-like conical flame, inclusion of the third dimension imparts the resolution of fluid mechanics and the interaction among the individual cones. As for developing the predictive models, four different supervised machine learning (ML) algorithms, decision tree (DT), random forest (RF), K-nearest neighbors (KNN), and artificial neural network (ANN), were employed to predict the i-v relationship. An experimental dataset of ≈ 10050 was utilized where a 60:20:20 split was adopted, allocating 60% for training, 20% for validation, and 20% for testing. It was concluded that charged 'sheaths' are formed at both torch and workpiece surfaces, subsequently forming three distinct regimes in the i-v relationship. The i-v characteristics obtained have been compared to the previous experimental study for premixed flame. In this way, the overall model generates a better understanding of the physical behavior of the oxyfuel cutting flames, along with a more validated i-v characteristics. Such understanding might provide critical information towards achieving an autonomous oxyfuel cutting process. / Doctor of Philosophy / Oxyfuel flame cutting is a century-old technique having widespread applications in heavy industries, including, but not limited to, building construction, defense, shipyards, etc. However, the mechanized oxyfuel cutting process has never benefited from the degree of autonomy due to contemporary sensing technologies' limitations at high-temperature working conditions. As a result, an experienced labor force is required to operate the system, thereby lowering the efficacy associated with this cutting process. A potential solution to this problem is motivated by preliminary measurements demonstrating that electrical events called 'ion currents' associated with the flame itself can reliably indicate vital process states. Provided that an autonomous process is achieved, this work could realize reliable cost-effective control of the oxyfuel cutting process, a capability of great interest to many core US industries involved in construction, and major equipment manufacture for defense and energy applications. Critical parameters (standoff, F/O ratio, flow rate, etc.) must be detected during operation to ensure an autonomous oxyfuel cutting process. The motivation stems from the fact that by measuring such co-dependence between critical parameters and electrical characteristics through a data acquisition unit (DAQ) and power supply, the shortcomings of sensing suites in a harsh operating environment can be compromised. Experimental data in the literature indicated the current-voltage (i-v) relationship with different critical parameters of oxyfuel flame to be the salient electrical characteristic in the preheating process when cutting steel. A comprehensive two-dimensional computational simulation using StarCCM+ only with the reduced combustion chemical mechanism with ion-exchange reactions has already been completed to elucidate the experimental results and to investigate the electrical characteristics such as ion migrations and ion distributions. Nonetheless, the findings exhibit some magnitude of differences compared to the experimental results. Thereby to further improve the results and better understand the underlying physics, further computational models using ANSYS FLUENT are proposed herein, having the reduced surface chemical mechanism considered. In addition, predictive models were developed based on machine learning (ML) algorithms. Four supervised ML algorithms - decision tree (DT), random forest (RF), Knearest neighbors (KNN), and artificial neural network (ANN) - were adopted to predict the current-voltage (i-v) relationship at different process states. ML offers a more data-driven, adaptable, and scalable approach to prediction compared to traditional methods. Its ability to handle large, noisy, and complex data makes it especially powerful for tasks that are challenging for conventional analytical techniques. The results of this study illustrate the detailed electrical characteristics of premixed methane-oxygen (CH4 – O2) oxyfuel cutting flame subject to an electric field, for both the computational fluid dynamics (CFD) and ML models. Since the physical flame is comprised of twelve Bunsen-like conical flame, inclusion of the third dimension will impart the resolution of fluid mechanics and the interaction among the individual cones. Moreover, the chemical activity at the work surface will also be considered, however, with a substantial simplification of the three-dimensional model as a cost. The overall model will generate a better understanding of the physical behavior of the oxyfuel cutting flames, along with a more validated currentvoltage (i-v) relationship. Consequently, this relationship could then be embedded into a control algorithm to detect the critical process parameters that may facilitate a step towards achieving an autonomous oxyfuel cutting process.

Research in the field of Photonics is in part, directed at the application of light-emitting materials based on silicon platforms. In this work silicon wafers are modified by carbon ion implantation to incorporate silicon carbide, a known light-emitting material. Ion beam synthesis treatments are applied with implant energy of 20 keV, and ion fluences of 3, 5 and 10 × 1016 ions/cm2 at both ambient temperature and high temperature (400 °C). The samples are annealed at 1000 °C, after implantation. The carbon ion implanted silicon is characterized using Raman and Fourier transform infrared spectroscopic techniques, grazing-incidence X-ray diffraction, transmission electron microscopy and electron energy loss spectroscopy. The materials are observed to have a multilayer structure, where the ambient temperature implanted materials have an amorphous silicon layer, and an amorphous silicon layer with carbon-rich, nanoscale inclusions. The high temperature implanted materials have the same layers, with an additional polycrystalline Si layer at the interface between the implanted layer and the target substrate and the amorphous Si layer with SiC inclusions is reduced in thickness compared to the ambient temperature samples. The carbon-rich inclusions are confirmed to be SiC, with no evidence of carbon clusters in the materials observed using Raman spectroscopy. The carbon ion-implanted material is used to fabricate Schottky diodes having a semitransparent gold contact at the implanted surface, and an aluminum contact on the opposite side. The diodes are tested using current-voltage measurements between -12 and +15 V. No reverse breakdown is observed for any of the diodes. The turn-on voltages for the ambient temperature implanted samples are 2.6±0.1 V, 2.8±0.6 V and 3.9±0.1 V for the 3, 5 and 10 × 1016 ions/cm2 samples, respectively. For the high temperature implanted samples, the turn-on voltages are 3.2±0.1 V, 2.7±0.1 V, and 2.9±0.4 V for the implanted samples with same fluences. The diode curves are modeled using the Shockley equation, and estimates are made of the ideality factor of the diodes. These are 188±16, 224.5±5.8, and 185.4±9.2 for the ambient temperature samples, and 163.6±6.3, 124.3±5.3, and 333±12 for the high temperature samples. The high ideality factor is associated with the native oxide layer on the silicon substrate and with the non-uniform, defect-rich implanted region of the carbon ion implanted silicon. Red-orange visible light emission from the diodes is observed with voltage greater than the turn-on voltage applied across the diodes. The luminescence for the ambient temperature samples is attributed to porous silicon, and amorphous silicon. The high temperature implanted samples show luminescence associated with porous silicon, nanocrystalline silicon carbide, and defects in silicon related to ion implantation. The luminescent intensity observed for the ambient temperature samples is higher than for the high temperature samples. The dominant luminescence feature in the carbon ion-implanted silicon material is porous silicon, which is described by quantum confinement of excitons in silicon.

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<p dir="ltr">This thesis explores the modeling and analysis of half-cut photovoltaic (PV) modules equipped with bypass diodes under various shading conditions. As solar energy becomes increasingly vital in the global energy landscape, understanding the impact of shading on PV system performance is crucial. Shading, whether from environmental factors like trees and clouds or from elements like buildings, chimneys, and wires, significantly affects the performance and longevity of solar panels. The research recreates various shading conditions on six monocrystalline residential PV panels, each equipped with 120 half-cut cells and three bypass diodes to collect a rich dataset using a Fluke SMFT-1000 I-V Curve Tracer. The I-V curves obtained from these tests were used to refine a simulation model for half-cut PV modules with bypass diodes developed in Simulink, which incorporates an equivalent circuit using the eight-parameter model of a PV cell. The Simulink model's optimization involved fine-tuning parameters such as photo-generated current (Iph), series resistance (Rs), shunt resistance (Rp), and temperature coefficients to closely match measured data. To validate the model’s applicability, the model was tested on PV panels from different manufacturers. Key findings demonstrate that half-cut technology significantly reduces power losses compared to conventional PV modules, particularly under partial shading conditions. The integration of bypass diodes further enhances performance by preventing hotspot formation and allowing unshaded portions of the panel to continue generating power. This study also briefly describes the existing solutions (microinverter, DC optimizer, global MPPT) for residential sites with severe shading.</p>