In this investigation, the primary objective was to study the nonlinear behavior of unbraced two-bay concrete frames and to determine the extent to which ultimate load theory or limit design can be applied to these structures. The frame behavior was investigated analytically by two methods. In the first method the frame stability equation was derived assuming that members of the frame possess an elasto-plastic moment-curvature relationship. This stability analysis was also carried out by another model consisting of a column attached to a linear spring and carrying the total frame load. The second method was through a computer program which took material and geometric nonlinearities of concrete frames into account. A model concrete frame, with a scale factor of approximately one-third was considered. Variable parameters were loading condition, column reinforcement ratio, and beam to column load ratio. For each frame, the gravity loads were increased proportionally until 75% of the frame ultimate capacity under gravity loads was reached. Then; while these gravity loads were held constant, lateral load was applied and increased to failure. The overall geometry, 21-in high columns and 84-in long beam, were kept the same for all of model frames investigated. The computer study and the stability model analysis indicated that all frames remained stable until four plastic hinges (two in each bay) formed, thus producing a combined sway mechanism. Based on the scope of this study, it appears that limit design may be employed for unbraced reinforced concrete structures.

Thinking exercises used in the Theory of Constraints (TOC) were used to find and remove constraints at the Merged Beams Experiment at Oak Ridge National Laboratory. The goal of this project was to significantly reduce the amount of time used to take a certain type of measurement during an experimental cycle. After the TOC exercises were used, a basic plan for change was discovered. Preliminary data were taken to establish a baseline of performance from which changes were made. Post-Modification was analyzed showing the project was a success. The overlying reasoning for this exercise was to prove successfully that continuous improvement techniques used in the manufacturing industry can also be successful in a research environment. After overcoming the differences in the goals between each environment, it can be concluded that this reasoning is justified.

Natural fibers have received attention in recent years because of their minimal environmental impact, reasonably good properties, and low cost. There is a wide variety of natural fibers suitable for composite applications, the most common of which is flax. Flax has advantages in tensile strength, light weight, and low cost over other natural fibers. As with other natural and synthetic fibers, flax is used to reinforce both thermoset and thermoplastic matrices. When flax is used in thermoplastic matrices, polypropylene and polyethylene are the main resins used. Although at first glance flax may seem to be a cheaper alternative to fiberglass, this may not necessarily be as advantageous as one would hope. A full economic valuation should be based on raw material costs and full processing costs. Although flax fibers used in composites are generally a waste product from linen flax, they require additional processing which can significantly reduce flax's economic advantage over glass. This paper attempts to place some measure of economic comparison coupled with property comparisons between natural (mainly flax) fibers and glass fibers. Our tests compare tensile, flexural, and drop impact properties, as well as heat sensitivity, and colorant acceptance.

In this study, friction spot welding was used to join DP980 steel sheet. Four different ultra-hard tool materials were used with the objective of determining which tool material produced the highest number of acceptable-strength welds. Three of the tools were composed of various mixtures of polycrystalline cubic Boron Nitride (PCBN), Tungsten, and Rhenium. These materials are referred to herein as Q60, Q70, and Q80, the "Qxx" designation denoting the percentage of the volume of the tool material composed of PCBN. The fourth tool tested was composed entirely of PCBN. The Q70 tool produced approximately 1100 welds of acceptable strength before average weld strength decreased below the acceptable value, and the Q60 tool produced approximately 600 welds of acceptable strength. The Q80 material did not produce any welds with strengths above the acceptable value. However, Q80 produced the greatest number of welds of consistent strength. The PCBN tool, being the hardest, also did not produce any welds of acceptable strength, and failed at 257 welds. This failure is presumed to be a result of a tool/parameter mismatch which caused excessive loads on the tool. This research revealed that the weld parameters and tool materials used in this study will not generally provide for feasibility of implementation in industry. Further advances in weld parameter selection, tool geometry, and tool materials will be necessary in order to make friction spot joining of high strength steels an economically viable option.

The thermo-mechanical effects on the strain evolution within an EB-PVD thermal barrier coating (TBC) is presented in this work using in-situ characterization. Synchrotron X-ray diffraction at sector 1-ID at the Argonne National Laboratory provided both qualitative and quantitative in-situ data on the strain evolution under a thermal cycle with mechanical loading. The results show that at a critical combination of temperature and load, the stress in the thermally grown oxide (TGO) layer in the TBC reaches a tensile region. These significant findings enhance existing literature showing purely compressive strains within the TGO where mechanical loads have been neglected. The results have important implications on the effects on the overall life of the coating. Depth resolved quantitative strain is presented as contour plots over a thermal cycle highlighting the complementary strains in the adjacent layers including the bond coat and the TBC with time and temperature. Systematic identification of the appropriate peaks within the multi-layer TBC system provides guidelines for future strain studies using high energy X-rays. Piezospectroscopic studies with applied mechanical loading are further presented as verification of the room temperature XRD data for future development of the method as an operational technique to be used outside the laboratory environment.

A non-invasive method to quantify the stress distribution in polymer-based materials is presented through the piezospectroscopic calibration of alumina-epoxy nanocomposites. Three different alumina volume fraction nanocomposites were created and loaded under uniaxial compression in order to determine the relationship between applied stress and the frequency shift of the R-lines produced by alumina under excitation. Quantitative values for six piezospectroscopic coefficients were obtained which represent the stress-sensing property of the nanocomposites. The results were applied to an alumina-filled adhesive in a single lap shear configuration demonstrating the capability of the technique to monitor R-line peak positions with high spatial resolution and assess the stress distribution within the material prior to failure. Additionally, particle dispersion and volume fraction were confirmed with spectral intensities, introducing a novel experimental method for the assessment of quality in manufacturing of such nanocomposites. Results were further used to initiate studies in determining the load transfer to the nanoparticles and assessing the fundamental driving mechanisms.

A number of technologies rely on the conversion of short laser pulses from one spectral domain to another. Efficient frequency conversion is currently obtained in ordered nonlinear optical materials and requires a periodic spatial modulation of their nonlinear coefficient which results in a narrow bandwidth. One can trade off efficiency for more spectral bandwidth by relaxing the strict phase-matching conditions and achieve nonlinear interaction in carefully engineered disordered crystalline aggregates, in a so-called random quasi-phase-matching (rQPM) process. In this dissertation, we examine appropriate fabrication pathways for (1-x)Pb(Mg1/3Nb2/3)O3-xPbTiO3 (PMN-PT) and ZnSe transparent ceramics for applications in the mid-IR. The main challenge associated with the fabrication of high transparency PMN-PT ceramics is to avoid the parasitic pyrochlore phase. The most effective method to suppress the formation of this undesired phase is to use magnesium niobate (MgNb2O6) as the starting material. We have found that, contrary to commercially available lead oxide powders, nanopowders synthesized in our lab by the combustion method help improve the densification of ceramics and their overall optical quality. The effects of dopants on the microstructure evolution and phase-purity control in PMN-PT ceramics are also investigated and show that La3+ helps control grain-growth and get a pure perovskite phase, thereby improving the samples transparency. With large second order susceptibility coefficients and wide transmission window from 0.45 to 21 μm, polycrystalline zinc selenide is also an ideal candidate material for accessing the MWIR spectrum through rQPM nonlinear interaction. We have investigated non-stoichiometric heat-treatment conditions necessary to develop adequate microstructure for rQPM from commercial CVD-grown ZnSe ceramics. We have been able to demonstrate the world's first optical parametric oscillation (OPO) based on rQPM in ZnSe transparent ceramic, enabling broadband frequency combs spanning 3-7.5 μm.

The world of additive manufacturing revolves around speed and repeatability. Inherently, the process of 3D printing is plagued with variability that fluctuates with every material and parameter modification. Without proper qualification standards, processes can never become stable enough to produce parts that may be used in aerospace, medical, and construction industries. These industries rely on high quality metrics in order to protect the lives of those who may benefit from them. To establish trust in a process, all points of variation must be controlled and accounted for every part produced. In instances where even the best process controls are enacted, there still may be situational unknowns that can cause detrimental defects, often on micron scales. Through in-situ monitoring techniques, such as visual or acoustic monitoring, a secondary level of quality assessment can be performed. This type of real time monitoring solution can be used in a variety of ways to help reduce scrap rate, increase overall quality, and improve the mechanical characteristics of a newly developing material. In this proposal, a goal was set to develop a system that can be a low-cost alternative to a comparable acoustic monitoring system. This design is meant to be a low fidelity concept that can alert a user of any potential anomalies within a build by detecting spikes in acoustic emissions. The overall success of this experiment is set on two conditions. First, the new low-cost system should be mountable on various types of machines. Second, this system should demonstrate some level of equivalency to a similar system. These two situations were successfully met as the system was able to provide indications of anomalies present within a build. The system was calibrated and tuned to be able to measure signals on a SLM 125 running 316L powder. Minor modifications to the code and system can make it adaptable to different types of equipment such as CNC’s, bandsaws, casting processes, and other advanced manufacturing equipment. The model can be attenuated to support higher or lower frequencies as well as different types of acoustic sensors, which demonstrates the vast potential that this system can provide for detecting different types of defects.

Research to increase the efficiency of conventional solar cells is constantly underway. The goal of this work is to increase the efficiency of conventional solar cells by incorporating quantum dot (QD) nanoparticles in the absorption mechanism. The strategy is to have the QDs absorb UV and fluoresce photons in the visible region that are more readily absorbed by the cells. The outcome is that the cells have more visible photons to absorb and have increased power output. The QDs, having a CdSe core and a ZnS shell, were applied to the solar cells as follows. First, the QDs were synthesized in an octadecene solution, then they were removed from the solution and finally they were dried and deposited into polydimethylsiloxane (PDMS) and the PDMS/QD composite is allowed to cure. The cured sample is applied to a silicon solar panel. The panel with the PDMS/QD application outputs 2.5% more power than the one without, under identical illumination by a tungsten halogen lamp, using QDs that fluoresce in the orange region. This work demonstrates the feasibility of incorporating QDs to increase the efficiency of conventional solar cells. Because the solar cells absorb better in the red region, future effort will be to use QDs that fluoresce in that region to further boost cell output.

Scientists can better understand wetlands environments by collecting data they are interested in via sensor networks. However the deployment of these sensor nodes manually can be disruptive to these sensitive environments. We develop a set of algorithms for autonomously differentiating land from water via aerial imagery using an unmanned aerial vehicle (UAV). The UAV takes a picture of the area, clusters, classifies, defines regions, and then communicates the regions to other UAVs responsible for deploying the sensor nodes. These UAVs run an algorithm to determine the optimal locations for sensor nodes such that they completely cover the regions and allow for communication between the nodes in the sensor network. Our classifier training algorithm identifies the best classifier using clusters and we compare its successful classification rate to a pixel-based approach and we see classification rates of 89.6%. This classifier feeds into our online algoorithm that the UAV successfully uses to classify the Calaveras River in California. In our simulations to determine the most effective algorithm for determining where the place the sensor nodes in a sensor network, we found Triangular Geometric Tessellation was the optimal algorithm, able to achieve 91.5% coverage in concave areas and 88.2% coverage in convex areas with relatively low computational complexity.