Suction stabilized floats have been implemented into a variety of applications such as supporting wind turbines in off-shore wind farms and for stabilizing cargo ships. This thesis proposes an alternative use for the technology in creating a system of suction stabilized floats equipped with real time location modules to help first responders establish a localized coordinate system to assist in rescues. The floats create a stabilized platform for each anchor module due to the inverse slack tank effect established by the inner water chamber. The design of the float has also been proven to be stable in most cases of amplitudes and frequencies ranging from 0 to 100 except for when the frequency ranges from 23 to 60 Hz for almost all values of the amplitude. The modules in the system form a coordinate grid based off the anchors that can track the location of a tag module within the range of the system using ultra-wideband communications. This method of location identification allows responders to use the system in GPS denied environments. The system can be accessed through an Android app with Bluetooth communications in close ranges or through internet of things (IoT) using a module as a listener, a Raspberry Pi and an internet source. The system has proven to identify the location of the tag in moderate ranges with an approximate accuracy of the tag location being 15 cm.

It remains unquestionable that space-based technology is an indispensable component of modern daily lives. Success or failure of space missions is largely contingent upon the complex system analysis and design methodologies exerted in converting the initial idea

into an elaborate functioning enterprise. It is for this reason that this dissertation seeks to contribute towards the search for simpler, efficacious and more reliable methodologies and tools that accurately model and analyze space systems dynamics. Inopportunely, despite the inimical physical hazards, space systems must endure a perturbing dynamical environment that persistently disorients spacecraft attitude, dislodges spacecraft from their designated orbital locations and compels spacecraft to follow undesired orbital trajectories. The ensuing dynamics’ analytical models are complexly structured, consisting of parametrically excited nonlinear systems with external periodic excitations–whose analysis and control is not a trivial task. Therefore, this dissertation’s objective is to overcome the limitations of traditional approaches (averaging and perturbation, linearization) commonly used to analyze and control such dynamics; and, further obtain more accurate closed-form analytical solutions in a lucid and broadly applicable manner. This dissertation hence implements a multi-faceted methodology that relies on Floquet theory, invariant center manifold reduction and normal forms simplification. At the heart of this approach is an intuitive system state augmentation technique that transforms non-autonomous nonlinear systems into autonomous ones. Two fitting representative types of space systems dynamics are investigated; i) attitude motion of a gravity gradient stabilized spacecraft in an eccentric orbit, ii) spacecraft motion in the vicinity of irregularly shaped small bodies. This investigation demonstrates how to analyze the motion stability, chaos, periodicity and resonance. Further, versal deformation of the normal forms scrutinizes the bifurcation behavior of the gravity gradient stabilized attitude motion. Control laws developed on transformed, more tractable analytical models show that; unlike linear control laws, nonlinear control strategies such as sliding mode control and bifurcation control stabilize the intricate, unwieldy astrodynamics. The pitch attitude dynamics are stabilized; and, a regular periodic orbit realized in the vicinity of small irregularly shaped bodies. Importantly, the outcomes obtained are unconventionally realized as closed-form analytical solutions obtained via the comprehensive approach introduced by this dissertation.

The goal of any solar photovoltaic (PV) system is to generate maximum energy throughout its lifetime. The parameters that can affect PV module power output include: solar irradiance, temperature, soil accumulation, shading, encapsulant browning, encapsulant delamination, series resistance increase due to solder bond degradation and corrosion and shunt resistance decrease due to potential induced degradation, etc. Several PV modules together in series makes up a string, and in a power plant there are a number of these strings in parallel which can be referred to as an array. Ideally, PV modules in a string should be identically matched to attain maximum power output from the entire string. Any underperforming module or mismatch among modules within a string can reduce the power output. The goal of this project is to quickly identify and quantitatively determine the underperforming module(s) in an operating string without the use of an I-V curve tracer, irradiance sensor or temperature sensor. This goal was achieved by utilizing Radiovoltmeters (RVM). In this project, it is demonstrated that the voltages at maximum power point (Vmax) of all the individual modules in a string can be simultaneously and quantitatively obtained using RVMs at a single irradiance, single module operating temperature, single spectrum and single angle of incidence. By combining these individual module voltages (Vmax) with the string current (Imax) using a Hall sensor, the power output of individual modules can be obtained, quickly and quantitatively.

In this work, different methods for fabrication of flexible sensors and sensor characterization are studied. Using materials and equipment that is unconventional, it is shown that different processes can be used to create sensors that behave like commercially available sensors. The reason unconventional methods are used is to cut down on cost to produce the sensors as well as enabling the manufacture of custom sensors in different sizes and different configurations. Currently commercially available sensors are expensive and are usually designed for very specific applications. By creating these same types of sensors using new methods and materials, these new sensors will show that flexible sensor creation for many uses at a fraction of the cost is achievable.

This research focused on how low-income communities in Ghana could convert Waste Vegetable Oil (WVO) into biodiesel to supplement their energy demands. The 2016 World Energy Outlook estimates that about 8 million Ghanaians do not have access to electricity while 82% of the population use biomass as cooking fuel. However, WVO is available in almost every home and is also largely produced by hotels and schools. There are over 2,700 registered hotels and more than 28,000 educational institutions from Basic to the Tertiary level. Currently, most WVOs are often discarded in open gutters or left to go rancid and later disposed of. Therefore, WVOs serve as cheap materials available in large quantities with a high potential for conversion into biodiesel and commercializing to support the economic needs of low-income communities. In 2013, a group of researchers at Kwame Nkrumah University of Science and Technology (KNUST) in Ghana estimated that the country could be producing between 82,361 and 85,904 tons of biodiesel from WVOs generated by hotels alone in 2015. Further analysis was also carried out to examine the Ghana National Biofuel Policy that was introduced in 2005 with support from the Ghana Energy Commission. Based on the information identified in the research, a set of recommendations were made to help the central government in promoting the biodiesel industry in Ghana, with a focus on low-income or farming communities. Lastly, a self-sustaining biodiesel production model with high potential for commercialization, was proposed to enable low-income communities to produce their own biodiesel from WVOs to meet their energy demands.

The microstructure development of Inconel alloy 718 (IN718) during conventional processing has been extensively studied and much has been discovered as to the mechanisms behind the exceptional creep resistance that the alloy exhibits. More recently with the development of large scale 3D printing of alloys such as IN718 a new dimension of complexity has emerged in the understanding of alloy microstructure development, hence, potential alloy development opportunity for IN718.

This study is a broad stroke at discovering possible alternate microstructures developing in Direct-Metal-Laser-Sintering (DMLS) processed IN718 compared to those in conventional wrought IN718. The main inspiration for this study came from creep test results from several DMLS IN718 samples at Honeywell that showed a significant

improvement in creep capabilities for DMLS718 compared to cast and wrought IN718 (Honeywell).

From this data the steady-state creep rates were evaluated and fitted to current creep models in order to identify active creep mechanisms in conventional and DMLS IN718 and illuminate the potential factors responsible for the improved creep behavior in DMSL processed IN718.

Because rapid heating and cooling can introduce high internal stress and impact microstructural development, such as gamma double prime formations (Oblak et al.), leading to differences in material behavior, DMLS and conventional IN718 materials are studied using SEM and TEM characterization to investigate sub-micron and/or nano-scale

microstructural differences developed in the DMLS samples as a result of their complex thermal history and internal stress.

The preliminary analysis presented in this body of work is an attempt to better understand the effect of DMLS processing in quest for development of optimization techniques for DMLS as a whole. A historical sketch of nickel alloys and the development of IN718 is given. A literature review detailing the microstructure of IN718 is presented. Creep data analysis and identification of active creep mechanisms are evaluated. High-resolution microstructural characterization of DMLS and wrought IN718 are discussed in detail throughout various chapters of this thesis. Finally, an initial effort in developing a processing model that would allow for parameter optimization is presented.

The primary goal of this thesis work is to determine the activation energy for encapsulant browning reaction of photovoltaic (PV) modules using outdoor field degradation data and indoor accelerated degradation data. For the outdoor field data, six PV modules fielded in Arizona (hot climate) over 21 years and four PV modules fielded in New York (cold climate) over 18 years have been analyzed. All the ten modules were manufactured by the same manufacturer with glass/EVA/cell/EVA/back sheet construction. The activation energy for the encapsulant browning is calculated using the degradation rates of short-circuit current (Isc, the response parameter), weather data (temperature, humidity, and UV, the stress parameters) and different empirical rate models such as Arrhenius, Peck, Klinger and modified Peck models. For the indoor accelerated data, three sets of mini-modules with the same construction/manufacturer as that of the outdoor fielded modules were subjected indoor accelerated weathering stress and the test data were analyzed. The indoor accelerated test was carried out in a weathering chamber at the chamber temperature of 20°C, chamber relative humidity of 65%, and irradiance of 1 W/m2 at 340nm using a xenon arc lamp. Typically, to obtain activation energy, the test samples are stressed at two (or more) temperatures in two (or more) chambers. However, in this work, it has been attempted to do the acceleration testing of eight mini-modules at multiple temperatures using a single chamber. Multiple temperatures in a single chamber were obtained using thermal insulators on the back of the mini-modules. Depending on the thickness of the thermal insulators with constant solar gain from the xenon lamp, different temperatures on the test samples were achieved using a single weathering chamber. The Isc loss and temperature of the mini-modules were continuously monitored using a data logger. Also, the mini-modules were taken out every two weeks and various characterization tests such as IV, QE, UV fluorescence and reflectance were carried out. Activation energy from the indoor accelerated tests was calculated using the short circuit current degradation rate and operating temperatures of the mini-modules. The activation energy for the encapsulant browning obtained from the outdoor field data and the indoor accelerated data are compared and analyzed in this work.

As the photovoltaic (PV) power plants age in the field, the PV modules degrade and generate visible and invisible defects. A defect and statistical degradation rate analysis of photovoltaic (PV) power plants is presented in two-part thesis. The first part of the thesis deals with the defect analysis and the second part of the thesis deals with the statistical degradation rate analysis. In the first part, a detailed analysis on the performance or financial risk related to each defect found in multiple PV power plants across various climatic regions of the USA is presented by assigning a risk priority number (RPN). The RPN for all the defects in each PV plant is determined based on two databases: degradation rate database; defect rate database. In this analysis it is determined that the RPN for each plant is dictated by the technology type (crystalline silicon or thin-film), climate and age. The PV modules aging between 3 and 19 years in four different climates of hot-dry, hot-humid, cold-dry and temperate are investigated in this study.

In the second part, a statistical degradation analysis is performed to determine if the degradation rates are linear or not in the power plants exposed in a hot-dry climate for the crystalline silicon technologies. This linearity degradation analysis is performed using the data obtained through two methods: current-voltage method; metered kWh method. For the current-voltage method, the annual power degradation data of hundreds of individual modules in six crystalline silicon power plants of different ages is used. For the metered kWh method, a residual plot analysis using Winters’ statistical method is performed for two crystalline silicon plants of different ages. The metered kWh data typically consists of the signal and noise components. Smoothers remove the noise component from the data by taking the average of the current and the previous observations. Once this is done, a residual plot analysis of the error component is performed to determine the noise was successfully separated from the data by proving the noise is random.

Measuring and estimating solar resource availability is critical for assessing new sites for solar energy generation. This includes beam radiation, diffuse radiation, and total incident radiation. Total incident radiation is pertinent to solar photovoltaic (PV) output and low-temperature solar thermal applications whereas beam radiation is used for concentrating solar power (CSP). Global horizontal insolation (GHI) data are most commonly available of any solar radiation measurement, yet these data cannot be directly applied to solar power generator estimation because solar PV panels and solar CSP collectors are not parallel to the earth’s surface. In absence of additional measured data, GHI data may be broken down into its constituent parts—diffuse radiation and beam radiation—using statistical techniques that incorporate explanatory variables such as the clearness index. This study provides a suite of methods and regression models to estimate diffuse radiation as a function of various explanatory variables using both piecewise and continuous fits. Regression analyses using the clearness index are completed for seven locations in the United States and four locations in other regions of the world. The multi-site analysis indicates that models developed using training data for a single location perform best in that location, yet general models can be created that perform reasonably well across any locality and then applied to estimate solar resource availability in new locations around the world. Results from the global and site-specific models perform better than the existing models in literature and indicate that models perform different in different sky conditions e.g. clear or cloudy sky. Results also show that continuous models perform equivalent or better than the piecewise models. Newly generated piecewise models showed improvement over some intervals in the clearness index. A combination of fits from this study and existing literature was used to improve overall performance of modeling techniques used in diffuse radiation estimation. Germany was selected for more detailed studies of a single case study using the clearness index, ambient temperature, relative humidity, and absolute humidity as explanatory variables. Clearness index is the most important variable for diffuse radiation calculation whereas the relative humidity and the temperature are the secondary variable for improving calculation. Absolute humidity plays similar role as temperature in improving the calculation on the other hand relative humidity improves it very slightly over the absolute humidity and temperature.

In the past 10 to 15 years, there has been a tremendous increase in the amount of photovoltaic (PV) modules being both manufactured and installed in the field. Power plants in the hundreds of megawatts are continuously being turned online as the world turns toward greener and sustainable energy. Due to this fact and to calculate LCOE (levelized cost of energy), it is understandably becoming more important to comprehend the behavior of these systems as a whole by calculating two key data: the rate at which modules are degrading in the field; the trend (linear or nonlinear) in which the degradation is occurring. As opposed to periodical in field intrusive current-voltage (I-V) measurements, non-intrusive measurements are preferable to obtain these two key data since owners do not want to lose money by turning their systems off, as well as safety and breach of installer warranty terms. In order to understand the degradation behavior of PV systems, there is a need for highly accurate performance modeling. In this thesis 39 commercial PV power plants from the hot-dry climate of Arizona are analyzed to develop an understanding on the rate and trend of degradation seen by crystalline silicon PV modules. A total of three degradation rates were calculated for each power plant based on three methods: Performance Ratio (PR), Performance Index (PI), and raw kilowatt-hour. These methods were validated from in field I-V measurements obtained by Arizona State University Photovoltaic Reliability Lab (ASU-PRL). With the use of highly accurate performance models, the generated degradation rates may be used by the system owners to claim a warranty from PV module manufactures or other responsible parties.