Browsing by Author "Padgett, Jamie E"

Now showing 1 - 7 of 7
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    Hypervelocity impact studies of carbon nanotubes and fiber-reinforced polymer nanocomposites
    (2014-04-24) Khatiwada, Suman; Barrera, Enrique V.; Ajayan, Pulickel M; Padgett, Jamie E
    This dissertation studies the hypervelocity impact characteristics of carbon nanotubes (CNTs), and investigates the use of CNTs as reinforcements in ultra-high molecular weight polyethylene (UHMWPE) fiber composites for hypervelocity impact shielding applications. The first part of this dissertation is aimed at developing an understanding of the hypervelocity impact response of CNTs – at the nanotube level. Impact experiments are designed with CNTs as projectiles to impact and crater aluminum plates. The results show that carbon nanotubes are resistant to the high-energy shock pressures and the ultra-high strain loading during hypervelocity impacts. Under our experimental conditions, single-walled carbon nanotubes survive impacts up to 4.07 km/s, but transform to graphitic ribbons and nanodiamonds at higher impact velocities. The nanodiamonds are metastable and transform to onion-like nanocarbon over time. Double-walled carbon nanotubes retain their form and structure even at impacts over 7 km/s. Higher hypervelocity impact resistance of DWCNTs could be attributed to the absorption of additional energy due to relative motion between the layers in the transverse direction of these coaxial nanotubes. The second part of this dissertation researches the effect of reinforcement of carbon nanotubes and their buckypapers on the hypervelocity impact shielding properties of UHMWPE-fiber composites arranged in a Whipple Shield configuration (a shield design used for the protection of the international space station from hypervelocity impacts by orbital debris). Composite laminates were prepared via compression molding and nanotube buckypapers via vacuum filtration. Dispersed nanotubes were introduced to the composite laminates via direct spraying onto the fabric prior to composite processing. The experimental results show that nanotubes dispersed in polymer matrix do not affect the hypervelocity impact resistance of the composite system. Nanotube buckypapers, however, improve the impact resistance of the composite, owing to the collective dampening of the shock wave amplitudes by the interconnected nanotube network in a buckypaper. The location of the buckypaper inside the composite, its thickness, and its surface modification with metals, all affect its hypervelocity impact shielding properties. Buckypaper coated with nickel and placed on the top surface of the UHMWPE-fiber composite provides the best impact resistance. Physical properties such as high bulk speed of sound in the nanotubes, and a combination of high density and high bulk speed of sound in nickel make the nickel-coated buckypaper a good hypervelocity impact shielding material. In addition, an explorative study on the use of nanograin metals for hypervelocity impact shielding was conducted.
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    Modeling Coastal Petrochemical Infrastructure Risk, Resilience, and Cascading Community Consequences
    (2024-04-09) Capshaw, Kendall Marie; Padgett, Jamie E
    Global oil and gas processing infrastructure already faces a significant threat of disruption due to annual coastal flooding of major refining and petrochemical centers, which is expected to further increase with the effects of climate change. U.S. Gulf Coast refineries account for over half of the total refining capacity of the nation, and Gulf Coast ethylene producing facilities account for approximately 90% of domestic ethylene production. However, less than a third of products refined in this region is used to supply local markets, and as the largest ethylene producer in the world, the U.S. exports the vast majority of ethylene domestically produced. Due to the highly centralized nature of the U.S. petrochemical distribution network, disruptions affecting Gulf Coast oil and gas processing facilities can have widespread impacts. In addition to the adverse economic and supply chain disruption effects of refining and petrochemical processing facility failures, pollutant emissions due to facility disruptions pose a significant threat to the public health of fenceline communities and the surrounding environment. This thesis presents a comprehensive framework for modeling coastal refining and petrochemical infrastructure risk and resilience when subjected to coastal hazards, such as hurricanes, severe storms, or sea level rise. This framework propagates modeled risk and resilience metrics across the regional, domestic, and global product distribution network to gain insight into how local disruptive events might impact the broader supply chain. Additionally, this framework models select community and environmental cascading consequences of oil and gas processing infrastructure failures to demonstrate the ties to potential local health hazards posed by these disruptive events to the vulnerable communities surrounding these facilities. First, this thesis considers the impacts that sea level rise projections might have on the annual flood risk to coastal refineries, and how regional disruptions propagate across the network. Both the annual regional risk in terms of expected production disruption under a range of climate scenarios, as well as the expected production disruption due to a major flood event impacting refining hubs of high importance are assessed throughout the 21st century. These risks are propagated across the network to model the global impact of coastal flood-induced refining disruptions. This analysis provides insights on the relative risks that different climate scenarios and flood events pose globally, informing potential mitigation and adaptation needs of critical facilities. Then, this thesis presents the development of a predictive model for the likelihood and expected duration of refinery shutdowns under hurricane hazards. Such models are currently lacking in the literature yet essential for risk modeling of the cascading consequences of refinery shutdown ranging from resilience analyses of petroleum networks to potential health effects on surrounding communities tied to startup and shutdown activities. A database of empirical refinery idle time duration and storm hazards data is developed, and statistical analyses are conducted to explore the relationship between refinery and storm characteristics and shutdown duration. The proposed method with the highest predictive accuracy is found to be a model comprised of a logistic regression binary classification component related to refinery shutdown potential and a Poisson distribution generalized linear model component related to idle time duration determination. To illustrate the utility of the newly developed model, a case study is conducted exploring the impact of two storms affecting the Houston Ship Channel and surrounding region. Both the regional refining resilience as well as the distribution network resilience are quantified. Next, this thesis presents the development of predictive models for likelihood and expected duration of petrochemical facility idle and restart times and expected resulting excess emissions quantities are proposed. As is the case with refinery infrastructure, these models are also presently lacking in the literature for petrochemical processing infrastructure. Development of these models would similarly afford opportunities for risk and resilience modeling of the cascading consequences of petrochemical complex shutdowns, which might also include applications in resilience analyses of regional petrochemical processing infrastructure or quantification of negative health impacts on fenceline communities. A database of empirical petrochemical facility characteristics, downtime, and hurricane hazards data is developed, and statistical analyses are conducted to investigate the relationship between facility and storm features and shutdown duration. The proposed method for expected shutdown modeling with the highest predictive accuracy is determined to be one comprised of a logistic regression binary classification component related to facility shutdown potential and a gamma distribution generalized linear model component related to idle time duration determination. Finally, these developed models are employed as input to a culminating framework for cascading consequence modeling of petrochemical processing infrastructure subjected to hurricane hazards. Overall, the proposed framework leverages Bayesian networks for predictive modeling and potential updating of facility shutdown and excess emissions quantification due to hurricane-induced facility failures. To illustrate the utility of the proposed framework, a case study is performed investigating the potential mitigative impact of the proposed Galveston Bay Park Plan on Houston Ship Channel regional refining and petrochemical processing risk and resilience and cascading air pollutant emissions risk. Such analyses expose community and regional impacts of facility failures and can support resilience improvement decisions.
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    Multi-threat Sustainability Assessment of Bridges and Bridge Networks
    (2019-04-19) Vishnu, Navya; Padgett, Jamie E; Subramanian, Devika
    Sustainable performance of critical infrastructure like bridges under both service loads and extreme events is of growing importance to the society. This performance of highway networks and their constituent bridges can be mapped to sustainability indicators like cost, embodied energy, carbon dioxide (CO2) emissions or resource utilization. With heightened load demands due to overweight trucks and natural hazards on the aging bridge infrastructure, an integrated multi-threat sustainability framework for bridges and bridge networks is essential. The existing sustainability quanti cation methods and approaches lack a joint sustainability assessment considering bridge vulnerabilities to hazards as well as truck loads, especially using a probabilistic approach. The primary goal of this study is the development of probabilistic frameworks to assist and evaluate sustainability on two levels: bridge and bridge network. Bridge sustainability is dependent on contributions from different life-cycle phases like construction, operation, maintenance, failure and demolition. Unlike past life-cycle studies, the probabilistic life-cycle sustainability analysis (LCS-A) framework proposed in this study considers life-cycle phase interactions as well as integrates post-repair performance of bridge components while exploring the distribution of sustainability costs to provide a more holistic view of an bridges sustainability. Interactions in the life-cycle emerge due to interventions such as maintenance activities which primarily benefit service load performance, but also enhance hazard performance, a previously unexplored secondary interaction effect from life-cycle studies. For bridges subjected to multiple hazard events in their lifetime, the impact of post-repair modification of bridge component behavior in LCS-A is significant and cannot be neglected. This study gathers the insights from bridge level LCS-A and integrates it into developing a probabilistic sustainability evaluation framework for highway networks as well. The inclusion of bridge failures due to both spatial variation of hazard as well as truck presence is a key advancement proposed through this research. The traffic simulation of overweight trucks on a bridge in the network is developed using the average daily truck traffic on the bridge and using extreme value theory to predict percentage of overweight trucks. The variation in hazard occurrence and intensity is captured by using a probabilistic suite of scenarios for the network. Advances are also made in the methodology to evaluate traffic emissions by incorporation of traffic flow modeling and fuel congestion into the network assessment. The LCS-A and network frameworks developed in this study are capable of handling various sources of uncertainties, with propagation of uncertainties facilitated by use of surrogate models when predicting bridge failures. In addition to developing probabilistic distributions of sustainability metrics, this study also recommends using probabilistic sensitivity analysis to understand how the uncertainty in the sustainability indicator is influenced by uncertainties in the input parameters. Such a detailed sensitivity analysis highlights opportunities for reducing uncertainties in sustainability outcomes by focusing on reducing the uncertainties in the most important input parameters. The sustainability framework developed as part of this thesis also can be used to de-aggregate bridge or network sustainability into contributions from their constituent components. A new sustainability informed component importance measure (SCIM) is proposed in this study that leverages the probabilistic nature of the sustainability contributions from individual components and maps it to system level sustainability consequences. The SCIMs proposed in this study is developed by adopting a flexible system failure based on a user-defined threshold on the system level sustainability indicator. The temporal evolution of component importance due to aging related deterioration or potential change in user-de fined thresholds are also incorporated into the SCIMs. The probabilistic frameworks developed in this thesis can support owners in their efforts to improve bridge or network sustainability,such as evaluating the impact of interventions or repairs on performance of bridge for future hazards or service loads and making upgrade decisions to minimize impacts of bridge failures to the surrounding natural and built environment. Moreover, stakeholders may pose sustainability objectives or intervention schedules for preferred risk threshold given new insight on the full probability distribution of sustainability outcomes.
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    Nanocomposite Material Properties Estimation and Fracture Analysis via Peridynamics and Monte Carlo Simulation
    (2015-04-23) Decklever, Jacob; Spanos, Pol D.; Meade, Andrew J; Padgett, Jamie E
    This thesis presents a numerical model for the estimation of nanocomposite material properties and fracture analysis. A non-uniform peridynamic grid is utilized to simulate the nanocomposites along with Monte Carlo simulation which models single walled carbon nanotube (SWCNT) distribution, dispersion, curvature, orientation, length, and diameter. First, a random microstructure is generated from the user inputs consisting of a polymer matrix and SWCNTs. The system is then solved via peridynamic techniques and post-processed to obtain the bulk mechanical properties. Utilizing Monte Carlo simulations, the mean effective modulus for a given set of input parameters is derived. Fracture analysis is performed using a single realization and quasi-static loading conditions via peridynamics allowing simultaneous and spontaneous propagating fractures. The model is validated against experimental data available in the open literature.
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    Resilience Assessment of Electric Grids and Distributed Wind Generation under Hurricane Hazards
    (2015-04-21) Mensah, Akwasi Frimpong; Dueñas-Osorio, Leonardo; Padgett, Jamie E; Subramanian, Devika; Stein, Robert M
    Modern society is increasingly dependent on high quality electricity for its economy, security, cultural life, public health, safety and governance. As such, ensuring the resilience of electric power systems against deliberate attacks and natural disasters is critical to the continuous function, particularly of urban cities. This thesis proposes an efficient for assessing the resilience of electric power systems under hurricane hazards. The thesis also explores the use of wind turbines as distributed generation to provide back-up electricity during hurricane-induced outages. The study develops computationally efficient models for evaluating outages in electric grids, while demonstrating their applicability through modeling a large real system subject to natural hazards, structural and system responses, and restoration processes. It employs a Bayesian networks approach and uses influence networks constructed via N-1 contingence Direct Current (DC) flow analyses to make the framework computationally tractable, time-efficient and amenable for real-time updating of information via data fusion in the future. The framework computes hurricane-induced customer outages in distributed 1 km2 blocks across the entire system, and simulates system restoration according to resource mobilization practices and sequences identified from historic events. The study uses the Harris County electric grid in Texas under Hurricane Ike in 2008 to illustrate the framework’s application. The framework yields system responses that are in agreement with observed outages, with a mean error of 15.4% in outages aggregated at the ZIP code level. Performance comparison of the proposed framework with two previously existing models shows that the model has a better prediction accuracy and requires a significantly lower computation time than the existing models. The model takes minute and half as compared to more than an hour required by the previous models to run 50 simulations. Having observed widespread outages in the electric power system, with some lasting several days or weeks before power restoration, the study also looked at the reliability of wind turbines to support their integration in the form of distributed generation in power systems. The study introduced a closed-form methodology for computing the system failure probabilities of wind turbines considering different failure event definitions. The methodology is enhanced to incorporate consequences such as downtimes and repair costs of individual component failures, and to determine the turbine unavailability or cost risks. It yields vital reliability information that could be readily used for planning maintenance and forecasting wind power outputs necessary for widespread distributed wind generation. Furthermore, the study examines the use of tuned liquid column dampers (TLCDs) to increase the reliability of wind turbines. Comparison of results for wind turbines with and without the damper shows that a baseline TLCD of 1% mass ratio significantly reduces the structural vibrations (by as much as 47%), and considerably decreases the unavailability probability of a turbine (by up to 8%). Armed with the resilience assessment model for power systems and the reliability analysis tools for wind turbines, the study also develops a probabilistic model for quantifying the impact of distributed wind generation (DWG) on an electric grid during hurricane-induced outage periods. The model incorporates energy adequacy assessment principles while accounting for the uncertainty in electricity demands and in power output due to variability in the wind resource, unavailability of DWG units owing to turbine failures, as well as component failures in the main utility system. An application of the model to Harris County’s power system equipped with turbines of total rated capacity of 1.8 GW shows that the DWG can provide back-up power to up to 85% of the customers in a distribution area which directly connects a DWG unit, while reducing the overall outages in the entire county by 8.5%. Thus, DWG can help improve the resilience of electric grids, support the rapid recovery of hurricane-affect communities and reduce economic losses associated with widespread and prolonged outages. In summary, the study provides computationally efficient tools for exploring a wide range of what-if scenarios in large real energy systems. The models can be readily adapted to consider other emerging technologies such as storage systems, vehicle grids, smart grids and micro grids in electric grid resilience assessments. Thus, they can support resilience-based decisions for hurricane preparedness and mitigation, and restoration strategies that could ensure rapid recovery of the systems. They support efforts in ensuring a reliable and a sustainable supply of electricity during normal conditions or in the immediate aftermath of hurricane events. The outage assessment model, for instance, is directly implemented in the City of Houston’s Storm Risk Calculator, an online tool that informs resident users about the local risks they face from an in-coming hurricane, and the city’s emergency managers in hurricane disaster management. '
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    Robust Optimal Guidance for Spacecraft Reorientation Maneuvers
    (2015-04-23) Svecz, Andrew John; Meade, Andrew J., Jr.; Spanos, Pol D; Padgett, Jamie E; Bhatt, Sagar A; Alaniz, Abran
    Spacecraft can be commanded to perform fuel-optimal attitude maneuvers subject to path constraints and specified boundary conditions using techniques from optimal control theory. This thesis presents solutions to two optimal maneuver guidance problems for increasing the robustness of space station Optimal Propellant Maneuvers (OPMs). An optimal maneuver is generated to avoid excess solar heating for use during high solar beta angle conditions. In cases where beta angle is not a factor, ground-based analysis can be reduced by implementing an on-orbit maneuver correction algorithm based on neighboring optimal control theory. This algorithm can be used to find an approximate optimal solution in the presence of uncertainties in the model or initial conditions for the maneuver, and allows on-orbit generation of maneuvers from a reference trajectory even when mass properties are changed due to the docking of visiting vehicles.
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    Vision Navigation Performance for Autonomous Orbital Rendezvous and Docking
    (2015-04-23) Dahlin, Eric J; Spanos, Pol D.; Meade, Andrew J; Padgett, Jamie E; Woffinden, David C
    This thesis demonstrates the potential of performing orbital rendezvous and docking using vision navigation. The vision navigation algorithm tracks both known and unknown target features to determine the relative position and attitude between a chaser and target spacecraft. By processing imagery generated from an optical sensor, various target features can be tracked to accurately determine the relative motion between two orbiting vehicles. This research adopts an architecture that uses an extended Kalman filter (EKF) to processes angle measurements to various target features as extracted from the vision navigation algorithm. One potential limitation to this approach is determining the image scale or range. A Monte Carlo simulation evaluates the performance of the navigation filter in a closed-loop guidance, navigation, and control (GNC) system. This research introduces strategies to overcome the resulting range dilemma and characterizes the performance of using vision navigation for autonomous orbital rendezvous and docking.