High-temperature mechanical behaviors of metal alloys and underlying microstructural variations responsible for such behaviors are essential areas of interest for many industries, particularly for applications such as jet engines. Anisotropic grain structures, change of preferred grain orientation, and other transformations of grains occur both during metal powder bed fusion additive manufacturing processes, due to variation of thermal gradient and cooling rates, and afterward during different thermomechanical loads, which parts experience in their specific applications, could also impact its mechanical properties both at room and high temperatures. In this study, an in-depth analysis of how different microstructural features, such as crystallographic texture, grain size, grain boundary misorientation angles, and inherent defects, as byproducts of electron beam powder bed fusion (EB-PBF) AM process, impact its anisotropic mechanical behaviors and softening behaviors due to interacting mechanisms. Mechanical testing is conducted for EB-PBF Ti6Al4V parts made at different build orientations up to 600°C temperature. Microstructural analysis using electron backscattered diffraction (EBSD) is conducted on samples before and after mechanical testing to understand the interacting impact that temperature and mechanical load have on the activation of certain mechanisms. The vertical samples showed larger grain sizes, with an average of 6.6 µm, a lower average misorientation angle, and subsequently lower strength values than the other two horizontal samples. Among the three strong preferred grain orientations of the α phases, <1 1 2 ̅ 1> and <1 1 2 ̅ 0> were dominant in horizontally built samples, whereas the <0 0 0 1> was dominant in vertically built samples. Thus, strong microstructural variation, as observed among different EB-PBF Ti6Al4V samples, mainly resulted in anisotropic behaviors. Furthermore, alpha grain showed a significant increase in average grain size for all samples with the increasing test temperature, especially from 400°C to 600°C, indicating grain growth and coarsening as potential softening mechanisms along with temperature-induced possible dislocation motion. The severity of internal and external defects on fatigue strength has been evaluated non-destructively using quantitative methods, i.e., Murakami’s square root of area parameter model and Basquin’s model, and the external surface defects were rendered to be more critical as potential crack initiation sites.

An Earned Value Management System (EVMS) is an organization’s system for project/program management that integrates a defined set of associated work scopes, schedules and budgets, allowing for effective planning, performance, and management control. A mature EVMS that is compliant with standards and guidelines, and that is applied in a positive social environment is critical to the overall success of large and complex projects and programs. However, a comprehensive and up-to-date literature review revealed a lack of a data-driven and consistent rating system that can gauge the maturity and the environment surrounding EVMS implementation. Therefore, the primary objective of this dissertation focuses on the EVMS maturity and environment, and investigates their impact on project performance. The author was one of the 41 research team members whose goal was to develop the novel rating system called Integrated Project/Program Management (IP2M) Maturity and Environment Total Risk Rating (METRR). Using a multi-method research approach, the rating system was developed based on a literature review of more than 600 references, a survey with 294 responses, focus group meetings, and research charrettes with more than 100 subject matter experts from the industry. Performance data from 35 completed projects and programs representing over $21.8 billion in total cost was collected and analyzed. The data analysis showed that the projects with high EVMS maturity and good EVMS environment outperformed those with low maturity and poor environment in key project performance measures. The contributions of this work includes: (1) developing definitions for EVM, EVMS and other research related terms, (2) determining the gaps in the EVMS literature, (3) determining the EVMS state of the practice in the industry, (4) developing a scalable rating system to measure the EVMS maturity and environment, (5) providing quantified evidence on the impact of EVMS maturity and environment on project performance, and (6) providing guidance to practitioners to gauge their EVMS maturity and environment for an enhanced project and program management integration and performance.

In-field characterization of photovoltaics is crucial to understanding performance and degradation mechanisms, subsequently improving overall reliability and lifespans. Current outdoor characterization is often limited by logistical difficulties, variable weather, and requirements to measure during peak production hours. It becomes a challenge to find a characterization technique that is affordable with a low impact on system performance while still providing useful device parameters. For added complexity, this characterization technique must have the ability to scale for implementation in large powerplant applications. This dissertation addresses some of the challenges of outdoor characterization by expanding the knowledge of a well-known indoor technique referred to as Suns-VOC. Suns-VOC provides a pseudo current-voltage curve that is free of any effects from series resistance. Device parameters can be extracted from this pseudo I-V curve, allowing for subsequent degradation analysis. This work introduces how to use Suns-VOC outdoors while normalizing results based on the different effects of environmental conditions. This technique is validated on single-cells, modules, and small arrays with accuracies capable of measuring yearly degradation. An adaptation to Suns-VOC, referred to as Suns-Voltage-Resistor (Suns-VR), is also introduced to complement the results from Suns-VOC. This work can potentially be used to provide a diagnostic tool for outdoor characterization in various applications, including residential, commercial, and industrial PV systems.

Due to the new and old challenges, modern-day market management systems continue ‎to evolve, including market reformulations, introducing new market products, and ‎proposing new frameworks for integrating distributed energy resources (DERs) into the ‎wholesale markets. Overall, questions is regarding how to reflect these essential changes in ‎the market models (design, reformulation, and coordination frameworks), design market-‎based incentive structures to adequately compensate participants for providing ancillary ‎services, and assess these impacts on market settlements.‎First, this dissertation proposes the concept of securitized-LMP to solve the issue of how ‎market participants should be compensated for providing N-1 reliability services. Then, ‎pricing implications and settlements of three state-of-art market models are compared. The ‎results show that with a more accurate representation of contingencies in the market ‎models, N-1 grid security requirements are originally captured; thereby, the value of service ‎provided by generators is reflected in the prices to achieve grid security.‎ Also, new flexible ramping product (FRP) designs are proposed for different market ‎processes to (i) schedule day-ahead (DA) FRP awards that are more adaptive concerning ‎the real-time (RT) 15-min net load changes, and (ii) address the FRP deployability issue in ‎fifteen-minute market (FMM). The proposed market models performance with enhanced ‎FRP designs is compared against the DA market and FMM models with the existing FRP ‎design through a validation methodology based on California independent system operator ‎‎(ISO) RT operation. The proposed FRP designs lead to less expected final RT operating ‎cost, higher reliability, and fewer RT price spikes.‎ Finally, this dissertation proposes a distribution utility and ISO coordination framework ‎to enable ISO to manage the wholesale market while preemptively not allowing ‎aggregators to cause distribution ‎system (DS) violations. To this end, this coordination ‎framework architecture utilizes the statistical information obtained using different DS ‎conditions and data-mining algorithms to predict the aggregators qualified maximum ‎capacity. A validation phase considering Volt-VAr support provided by distributed PV smart ‎inverters is utilized for evaluate the proposed model performance. The proposed model ‎produces wholesale market awards for aggregators that fall within the DS operational limits ‎and, consequently, will not impose reliable and safety issues for the DS.‎

Physical and structural tree measurements are applied in forestry, precision agriculture and conservation for various reasons. Since measuring tree properties manually is tedious, measurements from only a small subset of trees present in a forest, agricultural land or survey site are often used. Utilizing robotics to autonomously estimate physical tree dimensions would speed up the measurement or data collection process and allow for a much larger set of trees to be used in studies. In turn, this would allow studies to make more generalizable inferences about areas with trees. To this end, this thesis focuses on developing a system that generates a semantic representation of the topology of a tree in real-time. The first part describes a simulation environment and a real-world sensor suite to develop and test the tree mapping pipeline proposed in this thesis. The second part presents details of the proposed tree mapping pipeline. Stage one of the mapping pipeline utilizes a deep learning network to detect woody and cylindrical portions of a tree like trunks and branches based on popular semantic segmentation networks. Stage two of the pipeline proposes an algorithm to separate the detected portions of a tree into individual trunk and branch segments. The third stage implements an optimization algorithm to represent each segment parametrically as a cylinder. The fourth stage formulates a multi-sensor factor graph to incrementally integrate and optimize the semantic tree map while also fusing two forms of odometry. Finally, results from all the stages of the tree mapping pipeline using simulation and real-world data are presented. With these implementations, this thesis provides an end-to-end system to estimate tree topology through semantic representations for forestry and precision agriculture applications.

Object tracking refers to the problem of estimating a moving object's time-varying parameters that are indirectly observed in measurements at each time step. Increased noise and clutter in the measurements reduce estimation accuracy as they increase the uncertainty of tracking in the field of view. Whereas tracking is performed using a Bayesian filter, a Bayesian smoother can be utilized to refine parameter state estimations that occurred before the current time. In practice, smoothing can be widely used to improve state estimation or correct data association errors, and it can lead to significantly better estimation performance as it reduces the impact of noise and clutter. In this work, a single object tracking method is proposed based on integrating Kalman filtering and smoothing with thresholding to remove unreliable measurements. As the new method is effective when the noise and clutter in the measurements are high, the main goal is to find these measurements using a moving average filter and a thresholding method to improve estimation. Thus, the proposed method is designed to reduce estimation errors that result from measurements corrupted with high noise and clutter. Simulations are provided to demonstrate the improved performance of the new method when compared to smoothing without thresholding. The root-mean-square error in estimating the object state parameters is shown to be especially reduced under high noise conditions.

Construction project teams expend substantial effort to develop scope definition during the front end planning phase of building projects but oftentimes neglect to sufficiently plan for the complexities of tribal building projects. A needs assessment conducted by the author comprising interviews with practitioners familiar with construction on tribal lands revealed the need for a front end planning (FEP) process to assess scope definition of capital projects on tribal lands. This dissertation summarizes the motivations and efforts to develop a front end planning tool for tribal building projects, the Project Definition Rating Index (PDRI) for Tribal Building Projects. The author convened a research team to review, analyze, and adapt an existing building-projects-focused FEP tool, the PDRI – Building Projects, and other resources to develop a set of 67 specific elements relevant to the planning of tribal building projects. The author supported the facilitation of seven workshops in which 20 industry professionals evaluated the element descriptions and provided element prioritization data that was statistically analyzed to develop a preliminary weighted score sheet that corresponds to the element descriptions. Given that the author was only able to collect complete data from 11 projects, definitively determining element weights was not possible. Therefore, the author leveraged a Delphi study to test the PDRI – Tribal Building Projects. Delphi study results indicate the PDRI – Tribal Building Projects element descriptions fully address the scope of tribal building projects, and 75 percent of panelists agreed they would use this tool on their next tribal project. The author also explored the PDRI – Tribal Building Projects tool through the lens of the Diné (Navajo) Philosophy of Sa’ąh Naagháí Bik’eh Hózhóón (SNBH) and the guiding principles of Nistáhákees (thinking), Nahat’á (planning), Iiná (living), and Sihasin (assurance/reflection). The results of the author’s research provides several contributions to the American Indian Studies, front end planning, and tribal building projects bodies of knowledge: 1) defining unique features of tribal projects, 2) explicitly documenting the synergies between Western and Diné ways of planning, and 3) creating a tool to assist in planning capital projects on tribal lands in the American Southwest in support of improved cost performance.

Multibody Dynamic (MBD) models are important tools in motion analysis and are used to represent and accurately predict the behavior of systems in the real-world. These models have a range of applications, including the stowage and deployment of flexible deployables on spacecraft, the dynamic response of vehicles in automotive design and crash testing, and mapping interactions of the human body. An accurate model can aid in the design of a system to ensure the system is effective and meets specified performance criteria when built. A model may have many design parameters, such as geometrical constraints and component mechanical properties, or controller parameters if the system uses an external controller. Varying these parameters and rerunning analyses by hand to find an ideal design can be time consuming for models that take hours or days to run. To reduce the amount of time required to find a set of parameters that produces a desired performance, optimization is necessary. Many papers have discussed methods for optimizing rigid and flexible MBD models, and separately their controllers, using both gradient-based and gradient-free algorithms. However, these optimization methods have not been used to optimize full-scale MBD models and their controllers simultaneously. This thesis presents a method for co-optimizing an MBD model and controller that allows for the flexibility to find model and controller-based solutions for systems with tightly coupled parameters. Specifically, the optimization is performed on a quadrotor drone MBD model undergoing disturbance from a slung load and its position controller to meet specified position error performance criteria. A gradient-free optimization algorithm and multiple objective approach is used due to the many local optima from the tradeoffs between the model and controller parameters. The thesis uses nine different quadrotor cases with three different position error formulations. The results are used to determine the effectiveness of the optimization and the ability to converge on a single optimal design. After reviewing the results, the optimization limitations are discussed as well as the ability to transition the optimization to work with different MBD models and their controllers.

Thermal susceptibility is one of the biggest challenges that asphalt pavements must overcome. Asphalt mixture’s thermal susceptibility can increase problems related to permanent deformation, and the expansion-contraction phenomenon triggers thermal cracking. Furthermore, there is a common worldwide interest in environmental impacts and pavements. Saving energy and mitigating the urban heat island (UHI) effect have been drawing the attention of researchers, governments, and industrial organizations. Pavements have been shown to play an important role in the UHI effect. Globally, about 90% of roadways are made of asphalt mixtures. The main objective of this research study involves the development and testing of an innovative aerogel-based product in the modification of asphalt mixtures to function as a material with unique thermal resistance properties, and potentially providing an urban cooling mechanism for the UHI. Other accomplishments included the development of test procedures to estimate the thermal conductivity of asphalt binders, the expansion-contraction of asphalt mixtures, and a computational tool to better understand the pavement’s thermal profile and stresses. Barriers related to the manufacturing and field implementation of the aerogel-based product were overcome. Unmodified and modified asphalt mixtures were manufactured at an asphalt plant to build pavement slabs. Thermocouples installed at top and bottom collected data daily. This data was valuable in understanding the temperature fluctuation of the pavement. Also, the mechanical properties of asphalt binders and mixtures with and without the novel product were evaluated in the laboratory. Fourier transform infrared (FTIR) and scanning electron microscope (SEM) analyses were also used to understand the interaction of the developed product with bituminous materials. The modified pavements showed desirable results in reducing overall pavement temperatures and suppressing the temperature gradient, a key to minimize thermal cracking. The comprehensive laboratory tests showed favorable outcomes for pavement performance. The use of a pavement design software, and life cycle/cost assessment studies supported the use of this newly developed technology. Modified pavements would perform better than control in distresses related to permanent deformation and thermal cracking; they reduce tire/pavement noise, require less raw material usage during their life cycle, and have lower life cycle cost compared to conventional pavements.

The Discrete Fourier Transform (DFT) is a mathematical operation utilized in various signal processing applications including Astronomy and digital communications (satellite, cellphone, radar, etc.) to separate signals at different frequencies. Performing DFT on a signal by itself suffers from inter-channel leakage. For an ultrasensitive application like radio astronomy, it is important to minimize frequency sidelobes. To achieve this, the Polyphase Filterbank (PFB) technique is used which modifies the bin-response of the DFT to a rectangular function and suppresses out-of-band crosstalk. This helps achieve the Signal-to-Noise Ratio (SNR) required for astronomy measurements. In practice, 2N DFT can be efficiently implemented on Digital Signal Processing (DSP) hardware by the popular Fast Fourier Transform (FFT) algorithm. Hence, 2N tap-filters are commonly used in the Filterbank stage before the FFT. At present, Field Programmable Gate Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs) from different vendors (e.g. Xilinx, Altera, Microsemi, etc.) are available which offer high performance. Xilinx Radio-Frequency System-on-Chip (RFSoC) is the latest kind of such a platform offering Radio-frequency (RF) signal capture / generate capability on the same chip. This thesis describes the characterization of the Analog-to-Digital Converter (ADC) available on the Xilinx ZCU111 RFSoC platform, detailed design steps of a Critically-Sampled PFB, and the testing and debugging of a Weighted OverLap and Add (WOLA) PFB to examine the feasibility of implementation on custom ASICs for future space missions. The design and testing of an analog Printed Circuit Board (PCB) circuit for biasing cryogenic detectors and readout components are also presented here.