Browsing by Author "Radford, Donald, committee member"
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Item Open Access Evaluation of new reactive FRP reinforcement assemblies for reinforced concrete transportation structures(Colorado State University. Libraries, 2014) Bright, Christopher, author; van de Lindt, John W., advisor; Atadero, Rebecca, advisor; Radford, Donald, committee memberThis thesis evaluates two new glass-fiber reinforced polymer concrete reinforcement systems which have been designed to serve as a non-corrosive alternative to steel reinforcement in reinforced concrete bridge girders. Due to the nature of the reinforcement geometry, these systems react in a way to introduce compressive confinement into the concrete in the inner regions of the system units. The introduction of this compressive confinement zone will increase particle interaction effects which results in increased shear and tensile force resistance contributed by the affected concrete. The system is also well integrated into the surrounding concrete matrix, therefore eliminating the potential for debonding failures. A proof of concept is conducted in order to evaluate a set of alternative reinforcement system prototypes. Before the reinforcement systems are evaluated, technical literature pertaining to alternative reinforcements is reviewed. Select specimens provided evidence of sufficient mechanically constrictive behavior. Indications of good bond strength and shear strength contribution from the flexural reinforcement systems were also found. Parameters which control the structural behavior of the reinforcement system were identified.Item Open Access Manufacturing and testing of spline geometry using carbon fiber reinforced composite(Colorado State University. Libraries, 2016) Jambor, Eric, author; Bradley, Thomas, advisor; Radford, Donald, committee member; Heyliger, Paul, committee memberA model and manufacturing process for the design of carbon fiber reinforced composite spline shafts is developed and validated to investigate the feasibility of using composite splines for use in power transmission applications. Composite torque tubes for power transmission have been employed in various industries for over three decades and have shown up to a 50% mass decrease compared to steel shafts designed for the same use. One limiting factor for the amount of weight reduction achievable is the mechanism used to transfer power to and from the composite tube. Most composite shafts use adhesive bonding, fasteners, press fits, or some combination to join a steel or aluminum yolk or spline to the end of the tube. This research will investigate the feasibility of eliminating these mechanisms by replacing them by molding in splines to the composite torque tube. This will additionally reduce part count and manufacturing time as well as eliminating the heavy metal inserts. To achieve this, an analytical model is developed to investigate the strength of composite spline teeth of involute geometry as well as a composite torque tube. Due to the complex nature of designing with composites these models are supplemented by material models using a composite software package and finite element models (FEM). The involute splined shaft was then manufactured using an iterative approach to refine the sample quality and tested in torsion to failure. Although the peak failure torque had a large range over the samples it can be concluded that with improvements in the manufacturing process using molded composite splines is a feasible method of torque transfer. This can be concluded from the failure modes of the splined shaft as they indicate that the splines were able to adequately transfer the load to the torque tube.Item Open Access Mechanical studies of cadmium sulfide/cadmium telluride (CdS/CdTe) photovoltaic modules(Colorado State University. Libraries, 2015) Armijo, Mark Andrew, author; Sampath, Walajabad, advisor; Radford, Donald, committee member; Sites, James, committee memberCommercial Cadmium Sulfide (CdS) and Cadmium Telluride (CdTe) photovoltaic modules are typically 24” x 48”. The processing steps include: glass heating, Cadmium Sulfide (CdS) deposition and Cadmium Telluride (CdTe) deposition, Cadmium Chloride (CdCl2) heat treatment, back contact formation and back contact heat-treatment. The main components of the photovoltaic module under consideration in this research are the tempered front glass, an encapsulant (ethylene vinyl acetate (EVA)) interlayer, and the tempered bottom glass. During processing, the front glass loses a certain degree of temper. This results in the reduction of the residual stress within the front glass and ultimately reduces the strength of the module. The residual stress before and after processing was measured. The glass heating reduced the residual stress from 10,000 psi to approximately 2,500 psi. Even with the loss of the residual stress, the modules passed the static load test of 2,400 Pa and survived the hail impact test (1” ice balls at 50 mph). The mechanical behavior of the composite photovoltaic (PV) modules under static mechanical load and hail impact load utilizing mechanics modeling and experimental testing were characterized. The accuracy of the theoretical model is compared to the results of the experimental testing. The results will provide valuable knowledge for the mechanical characteristics of the PV module. This will contribute to the understanding of the effects of temper loss and whether the module exhibits a loss in strength.Item Open Access Mechanics of extendable wind turbine blades(Colorado State University. Libraries, 2015) John, Jeswin, author; Heyliger, Paul, advisor; Atadero, Rebecca, committee member; Radford, Donald, committee memberThis research aims at understanding the reductions in deflection, stress, and natural frequency of extendable wind turbine blades. For that purpose, a comparative study of these properties for the extendable turbine blade compared with those of a conventional turbine blade was completed. Wind turbine blades have seen extensive growth in application, and extendable turbine blades are a novel advancement over conventional blades. They can be more efficient in extracting energy from wind and are much more practical for transportation purposes. Lengths of the turbine blade have been increasing every year, and the next logical step is to consider making them extendable. In this research, a basic model of the blade was created and then a three-dimensional linear elasticity model was used and studied using the finite element method for analyzing the crucial parameters. In addition to this, two different load cases and six different retracted blade positions were analyzed for in-depth study of the blade behavior. As far as loading is considered, an initial analysis was completed using the wind load alone to give a basic idea of how the model behaves under standard parked conditions. In the second case, both wind and dead load were considered to help understand the blade behavior from a more practical perspective. Overall, the research gives estimates of the reductions in stress, displacement, and natural frequency when the blades are extendable and gives better understanding into the design parameters of these novel structures.Item Open Access Modeling deformation twinning in BCC transition metals(Colorado State University. Libraries, 2023) Faisal, Anik H. M., author; Weinberger, Christopher, advisor; Radford, Donald, committee member; Ma, Kaka, committee member; Heyliger, Paul, committee memberDeformation twinning is one of the important deformation mechanisms in body centered cubic (BCC) transition metals, especially under low temperature and high strain rate conditions. Plastic deformation via deformation twinning has been studied for decades both experimentally and computationally however, atomic level insights such as critical nuclei size, their local atomic structures and energetics which are important parameters in modeling twin nucleation has been lacking. In this work, deformation twins in BCC transition metals and their atomic level structures and energetics have been rigorously studied to reveal the full atomic level details of twin nucleation and propagation. As such, critical thickness of deformation twins in BCC transition metals have been a topic of debate with many computational and experimental studies accepting a three-layer twin thickness based on nucleation from a screw dislocation without proof whereas recent in-situ experiments suggest six-layer thick twin nuclei observed via High resolution transmission electron microscopy (HRTEM). In this study, we have determined the critical twin nuclei thickness in these metals using atomistic simulations to examine atomic structure and energetics of deformation twins under both zero and nonzero finite pure shear stresses. Our study reveals that twins in group VB BCC transition metals nucleate as two-layer thick nuclei under stress as opposed to the three-layer thick twin nuclei under zero stress. For group VIB BCC transition metals, for both zero and nonzero stresses, the critical twin nuclei thickness is two layer near reflection. As the twins grow and stress is relieved, twins under finite stresses adopt configurations that are much closer to the zero stress stability predictions. In addition to nucleation, growth of mechanisms of twins are explored and computational insights into the growth of twins in Tungsten bicrystals explaining multi-layer growth as opposed to layer-by-layer growth associated with small barriers. Free-end string simulations were used to investigate energy barrier associated with homogeneous twin nucleation using embedded atom method (EAM) potentials. Since homogeneous twin nucleation occurs near the ideal strengths of the material described by the potentials, energy barrier calculations were not possible for all BCC transition metals as some available potentials break down under large stresses. Moreover, density functional theory (DFT) simulations are known to be more accurate in describing atomic bonding but direct nucleation simulations in bulk crystals is prohibitively expensive. Hence, existing dislocation nucleation models are thoroughly analyzed to examine the behavior of these models near ideal strength of the material because spontaneous nucleation of dislocations occurs at high stresses. From there, a robust homogeneous twin nucleation model that includes elastic interaction among the twinning dislocation loops is developed which is able to replicate energy barrier data from free-end string simulations for multiple interatomic potentials. This model takes atomistic simulation inputs such as the concurrent twinning generalized stacking fault (GSF) energy curves and corresponding burgers vector of the twinning dislocations to compute the energy barriers as a function of applied stress. This model can be useful in modeling homogeneous twin nucleation all BCC transition metals and has the potential advantage of using DFT simulation inputs for accurate description of atomic bonding within the twin nuclei. Finally, nucleation stresses for twinning in bulk crystals have been studied to investigate whether the formation of twinning in experimental studies were initiated by homogeneous nucleation. Upper and lower bounds of stress values required for homogeneous twin nucleation has been computed and a semi-empirical model has been developed to predict homogeneous twin nucleation stresses as a function of temperature and strain rate. This analysis shows that reported critical resolved shear stress (CRSS) values in experimental studies are not associated with homogeneous twin nucleation despite some modeling studies claiming otherwise.Item Open Access Optimizing designer cognition relative to generative design methods(Colorado State University. Libraries, 2023) Botyarov, Michael, author; Miller, Erika, advisor; Bradley, Thomas, committee member; Forrest, Jeffrey, committee member; Moraes, Marcia, committee member; Simske, Steve, committee member; Radford, Donald, committee memberGenerative design is a powerful tool for design creation, particularly for complex engineering problems where a plethora potential design solutions exist. Generative design systems explore the entire solution envelope and present the designer with multiple design alternatives that satisfy specified requirements. Although generative design systems present design solutions to an engineering problem, these systems lack consideration for the human element of the design system. Human cognition, particularly cognitive workload, can be hindered when presented with unparsed generative design system output, thereby reducing the efficiency of the systems engineering life cycle. Therefore, the objective of this dissertation was to develop a structured approach to produce an optimized parsing of spatially different generative design solutions, derived from generative design systems, such that human cognitive performance during the design process is improved. Generative design usability foundation work was conducted to further elaborate on gaps found in the literature in the context of the human component of generative design systems. A generative design application was then created for the purpose of evaluating the research objective. A novel generative design solution space parsing method that leverages the Gower distance matrix and partitioning around medoids (PAM) clustering method was developed and implemented in the generative design application to structurally parse the generative design solution space for the study. The application and associated parsing method were then used by 49 study participants to evaluate performance, workload, and experience during a generative design selection process, given manipulation of both the quantity of designs in the generative design solution space and filtering of parsed subsets of design alternatives. Study data suggests that cognitive workload is lowest when 10 to 100 generative design alternatives are presented for evaluation in the subset of the overall design solution space. However, subjective data indicates a caution when limiting the subset of designs presented, since design selection confidence and satisfaction may be decreased the more limited the design alternative selection becomes. Given these subjective considerations, it is recommended that a generative design solution space consists of 50 to 100 design alternatives, with the proposed clustering parsing method that considers all design alternative variables.Item Open Access Plasma processing for nanostructured topographies(Colorado State University. Libraries, 2012) Riedel, Nicholas Alfred, author; Williams, John, advisor; Popat, Ketul, advisor; Radford, Donald, committee member; Reynolds, Melissa, committee memberPlasma and directed ion interactions with materials have been widely observed to create complex surface patterns on a micro- and nano- scale. Generally, these texturizations are byproducts of another intended application (such as a feature formation on a sputtering target) and patterning is considered inconsequential or even detrimental. This work examined the possibility of using these phenomena as primary methods for producing beneficial topographies. Specifically, investigations focused on the use of helium plasma exposure and directed ion etching to create nanostructured surfaces capable of affecting biological interactions with implanted materials. Orthogonal argon ion etching and low energy helium plasma texturization of titanium were considered for use on orthopedic and dental implants as a means of increasing osteoblast activity and bone attachment; and oblique angle etching was evaluated for its use in creating topographies with cell deterrent or anti-thrombogenic properties. In addition, the helium driven evolution of surface features on 6061 aluminum alloy was characterized with respect to ion energy and substrate temperature. These surfaces were then considered for ice phobic applications.Item Open Access Predicting fatigue life extension of steel reinforcement in RC beams repaired with externally bonded CFRP(Colorado State University. Libraries, 2014) Sobieck, Tyler, author; Atadero, Rebecca, advisor; Mahmoud, Hussam, advisor; Radford, Donald, committee memberA majority of the United States' transportation infrastructure is over 50 years old with one in nine bridges being considered structurally deficient. Fatigue damage accumulation in bridge structures, generated by cyclic loading of passing traffic, has led to shorter service lives. Over the past few decades studies have shown carbon fiber reinforced polymer (CFRP) repairs to be an effective means of reducing fatigue damage accumulation in reinforced concrete (RC) girders. Despite the abundant research, the results, specifically the increase in fatigue life, vary widely making it difficult to apply them directly to repair designs. Therefore, design codes and guidelines presently in use are insufficient in providing engineers with the proper information to determine the extended fatigue life of the RC bridges repaired with CFRP. Current design codes state FRP repairs should limit the stress range in the reinforcing bars below the threshold where fatigue cracks can propagate. The problem with this philosophy is it essentially designs an overly conservative system with an infinite fatigue life. The proposed approach follows a performance based design philosophy for which the engineer designs for a specified extension in service life by limiting the crack growth rate in the reinforcement so the critical crack length, for which fracture in the reinforcement would occur, is never reached in the extended life. In this thesis, the results of experimental fatigue testing of control and CFRP repaired RC beams are highlighted and the fatigue crack propagation rate in the steel reinforcement is assessed for different repair schemes. The focus on steel reinforcement crack propagation rates was made because similar studies have found the reinforcement to be the limiting fatigue component in RC bridge girders. The results of the experimental study showed an extended fatigue life and a slowed crack growth rate in specimens repaired with both CFRP systems. The crack growth rates were then used to determine the material constants for the Pairs Law, which describes growth of a stable fatigue crack. These results were then used to propose recommendations for design of FRP repair systems for RC flexural members for a specific fatigue life.Item Open Access Stresses and frequency shifts in fully extended and folded wind turbine blades(Colorado State University. Libraries, 2017) Abdalrwaf, Wael, author; Heyliger, Paul, advisor; Radford, Donald, committee member; Atadero, Rebecca, committee memberAlternative methods for generating energy have grown in the application in the past few decades. The main objective of this research is to understand the changes in the displacements, stresses, and natural frequencies of fully extended and folded wind turbine blades. A comparative study of the folded blade of fitted properties with the fully extended wind turbine blade was achieved. Folded blades could be more efficient in generating electricity from the wind for turbines with small radii and could be beneficial for transportation purposes. In this study, a basic model of fully extended and folded blades was completed using three-dimensional linear elasticity model and the finite element method. Two different load cases were analyzed to study the conventional and folded blade behaviors. By using the wind load alone, an initial analysis is achieved as the wind load is applied to observe the blade behavior under standard conditions. For more practical consideration, both wind and gravity load were then applied. The study estimates the changes in stresses, displacements, and natural frequencies when the blades are folded and helps better understanding the necessary design parameters of these structures. Finally, free vibration behavior of the folded and extended blades is considered.Item Open Access System understanding of high pressure die casting process and data with machine learning applications(Colorado State University. Libraries, 2021) Blondheim, David J., Jr., author; Anderson, Charles, advisor; Simske, Steve, committee member; Radford, Donald, committee member; Kirby, Michael, committee memberDie casting is a highly complex manufacturing system used to produce near net shape castings. Although the process has existed for more than hundred years, a systems engineering approach to define the process and the data die casting can generate each cycle has not been completed. Industry and academia have instead focused on a narrow scope of data deemed to be the critical parameters within die castings. With this narrow focus, most of the published research on machine learning within die casting has limited success and applicability in a production foundry. This work will investigate the die casting process from a systems engineering perspective and show meaningful ways of applying machine learning. The die casting process meets the definition of a complex system both in technical definition and in the way that humans interact within the system. From the technical definition, the die casting system is a network structure that is adaptive and can self-organize. Die casting also has nonlinear components that make it dependent on initial conditions. An example of this complexity is seen in the stochastic nature of porosity formation, even when all key parameters are held constant. Die casting is also highly complex due to the human interactions. In manufacturing environments, human's complete visual inspection of castings to label quality results. Poor performance creates misclassification and data space overlap issues that further complicate supervised machine learning algorithms. The best way to control a complex system is to create feedback within that system. For die casting, this feedback system will come from Industry 4.0 connections. A systems engineering approach will define the critical process and then create groups of data in a data framework. This data framework will show the data volume is several orders of magnitude larger than what is currently being used within the industry. With an understanding of the complexity of die cast and a framework of available data, the challenge becomes identifying appropriate applications of machine learning in die casting. The argument is made, and four case studies show, unsupervised machine learning provides value by automatically monitoring the data that can be obtained and identifying anomalies within the die cast manufacturing system. This process control improvement thereby removes the noise from the system, allowing one to gain knowledge about the die casting process. In the end, the die casting industry can better understand and utilize the data it generates with machine learning.