Summer Research Experience Poster Session 2022

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Held in-person on the UTSA campus on Thursday, July 28th from 2-3:30pm.


Recent Submissions

Now showing 1 - 8 of 8
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    Identification of Pd-Si Compounds in Diffusion Couple Studies to Complement AGR PIE reports
    (2022-07-28) Stone, Jordan; Cavazos, Steven; Montoya, Katherine; Kirtley, Thomas; George, Gisele; Sooby, Elizabeth, S.
    Palladium (Pd) and silver (Ag) are known fission products of TRISO particle nuclear fuels [1]. Pd is known to corrode the silicon carbide (SiC) layer of a TRISO particle, which can compromise the overall fuel performance [2,3]. This study sets out to identify Pd-Si formations in SiC in order to better understand the corrosion of the SiC layer, and therefore the diffusion of Ag out of the TRISO layers found in AGR PIE reports. As of present, Pd has been observed to degrade SiC at 1000 °C; the extent of Pd diffusion and Pd-SiC compounds have yet to be determined.
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    Understanding of Additive Manufacturing with the Combination of the Flexible Manufacturing Process
    (2022-07-28) Perkins, Briley; Ewuzie, Emmanuel; Aristizabal, Mauricio; Balcer, Matt; Millwater, Henry
    Additive Manufacturing is 3D printing which requires a set of repeating steps throughout the process. STL, or Standard Triangle Language, is the most important step in the printing process that allows the model to be "sliced" the fastest and most proficient way, and allows the entire model to be modified at any step of the process. Although the printing process is the same for all the parts, some parts may be printed differently.
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    Additive Manufacturing & G Code
    (2022-07-28) Ewuzie, Emmanuel; Perkins, Briley; Aristizabal, Mauricio; Balcer, Matthew; Millwater, Harry
    Additive Manufacturing (AM) is the process of building physical objects by layering materials. It is controlled by G code and a myriad of other codes. CAD models are sliced and converted into a layer by layer code (G Code). This is then read by the 3D printer and executed. G code tells the motors where to move, how fast to move, and what path to follow.
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    Strategic Freezing
    (2022-07-28) Seligman, Zachary; Patrick, David; Fernandez, Amanda
    Convolutional neural networks (CNNs) are notoriously data-intensive, requiring significantly large datasets for training accurately in an appropriate runtime. Recent approaches aiming to reduce this requirement focus on removal of low-quality samples in the data or unimportant filters, leaving a vast majority of the training set and model in tact. We propose Strategic Freezing, a new training strategy which strategically freezes features in order to maintain class retention. Preliminary results of our approach are demonstrated on the Imagenette dataset using ResNet34.
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    Identifying Gamma Radiation Anomaly Signals Using Quantum Computation Methods
    (2022-07-28) Foate, Joshua; Valdez, Luis; Alamaniotis, Miltos
    Collected data using a radiation detector to identify anomalies in the presence of naturally occurring radioactive material. Samples of the anomaly signals were put into a Hopfield neural network to train the network to identify whether data from the detector was an anomaly or background radiation. Converting our data into a 3SAT (3- Satisfiability) problem and use Grover's algorithm to find the solutions for Hopfield Artificial Neural Network memory
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    Semantic Segmentation for Materials Classification of Nuclear Fuels
    (2022-07-28) Mohanadhas, Daniel; Snyder, Chris; Fernandez, Amanda
    Semantic segmentation, the task of classifying objects in an image at a pixel level, has been done since 2012. While our method is not new, our application is. Unlike most tasks which are on clearly-defined objects, the dataset we attempt to label is like Perlin Noise: seemingly random but with clear patterns throughout. Additionally, we had a very small dataset to work with, but preliminary results show that approaches used on more standard applications also work well in this novel application.
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    Probe Beam Deflection Technique as a Characterization Method for Nuclear Materials
    (2022-07-28) Carle, Lydia; Escudero, Jose; Flowers, Jacob; Nash, Kelly
    Probe beam deflection technique (PBDT) was evaluated as a potential method of characterization for both radioactive and non radioactive materials. This method was chosen for its non destructive nature. The analyte of interest was UB 2 . Alternative fuel sources for nuclear reactors are being investigated as additives or replacements for UO 2 [4]; UB 2 was chosen for this purpose. CeO 2 was used because it is a surrogate for UO 2 . No correlation was found between signal intensity and Eu doping concentration was found.
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    Fabrication of mini UB2 ingots via Arc melt synthesis using a customized copper hearth
    (2022-07-28) Facundo, Jesus, U; Montoya, Katherine; Kirtley, Thomas; Sooby, Elizabeth S.
    As growing efforts take place to enhance the operational safety of nuclear reactors, fuel composites have been explored as replacement to the traditionally used Uranium dioxide (UO2). One potential candidate that has been gaining momentum as a fuel composite additive is Uranium diboride. UB2 is known to have a higher uranium density and higher thermal conductivity than UO2, properties that would allow for a lower enrichment of the fuel pellets as well as improve the temperature gradient across the pellet during reactor operation. While various challenges arise when considering UB2 as a drop-in replacement to UO2, UB2 has shown much promise as a composite fuel when combined with other uranium compounds such as U3Si2.Through the use of an arc-melter system, 50-250 mg ingots of UB2 were fabricated using the fragments of a larger 2-5 g ingot of UB2. X-Ray diffraction analysis was performed to confirm the purity of the initial UB2 ingot. Further, an infrared camera was used to monitor the temperature of the furnace chamber during the mini-UB2 bead fabrication. The purpose of this project is to understand the fabrication process of UB2 and characterize the micro-structure of the as-fabricated mini fuel beads. We wish to better understand the viability of UB2 as a potential fuel composite additive.