Power generation through heat to electrical energy conversion for space applications faces distinct challenges not encountered in terrestrial settings, where Rankine and Brayton cycles have traditionally been predominant. The unique environment of space necessitates the adoption of either static converters, leveraging solid-state physics, or closed-cycle dynamic converters. While thermoelectric generators have historically been the primary choice for heat-to-electrical energy conversion in space applications, their relatively low efficiencies and limited scope for enhancement pose significant challenges as the power demands of space missions increase. This necessitates the exploration of alternative power generation methodologies to meet the evolving requirements. This thesis provides a comprehensive analysis of various power conversion technologies for space applications, focusing on the comparative study of static and dynamic converters, with a particular emphasis on Stirling converters. Other power systems discussed include thermoelectric, thermophotovoltaic, thermionic, and Brayton converters. Through comparative analysis, the research identifies the most promising converters for future space applications.

Metal-Oxide-Semiconductor (MOS) is essential to modern VLSI devices. In the past decades, a wealth of literature has been created to understand the impact of the radiation-induced charges on the devices, i.e., the creation of electron-hole pairs in the oxide layer which is the most sensitive part of MOS structure to the radiation effect. In this work, both MOS and MNOS devices were fabricated at ASU NanoFab to study the total ionizing dose effect using capacitance-voltage (C-V) electrical characterization by observing the direction and amounts of the shift in C-V curves and electron holography observation to directly image the charge buildup at the irradiated oxide film of the oxide-only MOS device.C-V measurements revealed the C-V curves shifted to the left after irradiation (with a positive bias applied) because of the net positive charges trapped at the oxide layer for the oxide-only sample. On the other hand, for nitride/oxide samples with positive biased during irradiation, the C-V curve shifted to the right due to the net negative charges trapped at the oxide layer. It was also observed that the C-V curve has less shift in voltage for MNOS than MOS devices after irradiation due to the less charge buildup after irradiation. Off-axis electron holography was performed to map the charge distribution across the MOSCAP sample. Compared with both pre-and post-irradiated samples, a larger potential drop at the Si/SiO2 was noticed in post-irradiation samples, which indicates the presence of greater amounts of positive charges that buildup the Si/SiO2 interface after the TID exposure. TCAD modeling was used to extract the density of charges accumulated near the SiO2/Si and SiO2/ Metal interface by matching the simulation results to the potential data from holography. The increase of near-interface positive charges in post-irradiated samples is consistent with the C-V results.

With the continued increase in the amount of renewable generation in the formof distributed energy resources, reliability planning has progressively become a more challenging task for the modern power system. This is because with higher penetration of renewable generation, the system has to bear a higher degree of variability and uncertainty. One way to address this problem is by generating realistic scenarios that complement and supplement actual system conditions. This thesis presents a methodology to create such correlated synthetic scenarios for load and renewable generation using machine learning. Machine learning algorithms need to have ample amounts of data available to them for training purposes. However, real-world datasets are often skewed in the distribution of the different events in the sample space. Data augmentation and scenario generation techniques are often utilized to complement the datasets with additional samples or by filling in missing data points. Datasets pertaining to the electric power system are especially prone to having very few samples for certain events, such as abnormal operating conditions, as they are not very common in an actual power system. A recurrent generative adversarial network (GAN) model is presented in this thesis to generate solar and load scenarios in a correlated manner using an actual dataset obtained from a power utility located in the U.S. Southwest. The generated solar and load profiles are verified both statistically and by implementation on a simulated test system, and the performance of correlated scenario generation vs. uncorrelated scenario generation is investigated. Given the interconnected relationships between the variables of the dataset, it is observed that correlated scenario generation results in more realistic synthetic scenarios, particularly for abnormal system conditions. When combined with actual but scarce abnormal conditions, the augmented dataset of system conditions provides a better platform for performing contingency studies for a more thorough reliability planning. The proposed scenario generation method is scalable and can be modified to work with different time-series datasets. Moreover, when the model is trained in a conditional manner, it can be used to synthesise any number of scenarios for the different events present in a given dataset. In summary, this thesis explores scenario generation using a recurrent conditional GAN and investigates the benefits of correlated generation compared to uncorrelated synthesis of profiles for the reliability planning problem of power systems.

Information about the elemental composition of a planetary surface can be determined using nuclear instrumentation such as gamma-ray and neutron spectrometers (GRNS). High-energy Galactic Cosmic Rays (GCRs) resulting from cosmic super novae isotropically bombard the surfaces of planetary bodies in space. When GCRs interact with a body’s surface, they can liberate neutrons in a process called spallation, resulting in neutrons and gamma rays being emitted from the planet’s surface; how GCRs and source particles (i.e. active neutron generators) interact with nearby nuclei defines the nuclear environment. In this work I describe the development of nuclear detection systems and techniques for future orbital and landed missions, as well as the implications of nuclear environments on a non-silicate (icy) planetary body. This work aids in the development of future NASA and international missions by presenting many of the capabilities and limitations of nuclear detection systems for a variety of planetary bodies (Earth, the Moon, metallic asteroids, icy moons). From bench top experiments to theoretical simulations, from geochemical hypotheses to instrument calibrations—nuclear planetary science is a challenging and rapidly expanding multidisciplinary field. In this work (1) I describe ground-truth verification of the neutron die-away method using a new type of elpasolite (Cs2YLiCl6:Ce) scintillator, (2) I explore the potential use of temporal neutron measurements on the surface of Titan through Monte-Carlo simulation models, and (3) I report on the experimental spatial efficiency and calibration details of the miniature neutron spectrometer (Mini-NS) on board the NASA LunaH-Map mission. This work presents a subset of planetary nuclear science and its many challenges in humanity's ongoing effort to explore strange new worlds.

The penetration of renewable energy in the power system has grown considerably in the past few years. While this use may come with an abundance of advantages, it also introduces new challenges in operating the 100+ years old electrical network. Fundamentally, the power system relies on a real-time balance of generation and demand. However, renewable resources such as solar and wind farms are not available throughout the day. Furthermore, they introduce temporal variability to the generation process due to metrological factors, making the balance of generation and demand precarious. Utilities use standby units with reserve power and high ramp-up, ramp-down capabilities to ensure balance. However, such solutions can be very costly. An accurate scenario generation and forecasting of the stochastic variables (load and renewable resources) can help reduce the cost of these solutions. The goal of this research is to solve the scenario generation and forecasting problems using state-of-the-art machine learning techniques and algorithms. The training database is created using publicly available data obtained from NREL and the Texas-2000 bus system. The IEEE-30 bus system is used as the test system for the analysis conducted here. The conventional generators of this system are replaced with solar farms and wind farms. The ability of four machine learning algorithms in addressing the scenario generation and forecasting problems are investigated using appropriate metrics. The first machine learning algorithm is the convolutional neural network (CNN). It is found to be well-suited for the scenario generation problem. However, its inability to capture certain intricate details about the different variables was identified as a possible drawback. The second algorithm is the long-short term memory-variational auto-encoder (LSTM-VAE). It generated scenarios that are very similar to the actual scenarios indicating that it is suitable for solving the forecasting problem. The third algorithm is the conditional generative adversarial network (C-GAN). It was extremely effective in generating scenarios when the number of variables were small. However, its scalability was found to be a concern. The fourth algorithm is the spatio-temporal graph convolutional network (STGCN). It was found to generate representative correlated scenarios effectively.

Three-Phase brushless DC motors (BLDC) have become increasingly popular in many fields including industrial controls and remote-control hobby toys. They offer many advantages over their brushed counterparts such as smaller size, longer service life, and increased efficiency; however, one drawback is that commutation must be handled electrically using a controller rather than by a mechanical commutator. Rotor position must be estimated in order to accurately commutate the motor, this is calculated either by sensors (sensored) or by measuring the generated Back-Electromotive Force (sensorless). There are two primary methods of brushless DC motor commutation, trapezoidal and sinusoidal. Both methods have advantages and disadvantages, as well as unique sets of rotor position estimation strategies. This paper will discuss in detail the development of a novel motor control algorithm that employs one method of sensorless trapezoidal control of BLDC motors where the BEMF is integrated after a zero-crossing event, the various challenges associated with direct BEMF measurement, and demonstrate a practical implementation of the new algorithm. Using a robust, high frequency sampling scheme and on-the-fly detection strategies, this new algorithm overcomes many of the shortcomings of similar control algorithms currently available on the market. As a result, this new algorithm provides even more robust control over BLDC motors, increased efficiency, and improved dynamic performance compared to its counterparts while simultaneously requiring little to no additional hardware in practical implementations. Topics investigated include BLDC motors, sensored and sensorless rotor estimation, PWM strategies, terminal voltage sensing, third harmonic voltage sensing and integration, sample timing, switching noise, and current recirculation.

Large-scale blackouts that have occurred across North America in the past few decades have paved the path for substantial amount of research in the field of security assessment of the grid. With the aid of advanced technology such as phasor measurement units (PMUs), considerable work has been done involving voltage stability analysis and power system dynamic behavior analysis to ensure security and reliability of the grid. Online dynamic security assessment (DSA) analysis has been developed and applied in several power system control centers. Existing applications of DSA are limited by the assumption of simplistic load profiles, which often considers a normative day to represent an entire year. To overcome these aforementioned challenges, this research developed a novel DSA scheme to provide security prediction in real-time for load profiles corresponding to different seasons. The major contributions of this research are to (1) develop a DSA scheme incorporated with PMU data, (2) consider a comprehensive seasonal load profile, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA. The ML algorithms that will be the focus of this study include decision trees (DTs), support vector machines (SVMs), random forests (RFs), and multilayer neural networks (MLNNs).

This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.

The intention of this report is to use computer simulations to investigate the viability of two materials, water and polyethylene, as shielding against space radiation. First, this thesis discusses some of the challenges facing future and current manned space missions as a result of galactic cosmic radiation, or GCR. The project then uses MULASSIS, a Geant4 based radiation simulation tool, to analyze the effectiveness of water and polyethylene based radiation shields against proton radiation with an initial energy of 1 GeV. This specific spectrum of radiation is selected because it a component of GCR that has been shown by previous literature to pose a significant threat to humans on board spacecraft. The analysis of each material indicated that both would have to be several meters thick to adequately protect crew against the simulated radiation over a several year mission. Additionally, an analysis of the mass of a simple spacecraft model with different shield thicknesses showed that the mass would increase significantly with internal space. Thus, using either material as a shield would be expensive as a result of the cost of lifting a large amount of mass into orbit.

Pacemakers in the early 1970s were powered by betavoltaic devices which provided long lasting battery life. The betavoltaic devices also emitted gamma radiation due to inadvertent radioisotope contamination, which could not be completely shielded. The betavoltaic devices were quickly replaced by lithium batteries after their invention, and betavoltaics were abandoned. Modern technological advancements made it possible to isolate beta emitting radioisotopes properly and achieve better energy conversion efficiencies which revived the topic of betavoltaics. This research project has studied state-of-the-art pacemakers and modern radioactive power sources in order to determine if modern pacemakers can be safely nuclear powered and if that is a reasonable combination.