Works of Scholarship

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https://hdl.handle.net/20.500.14216/72

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Now showing 1 - 16 of 16

A Comprehensive Approach to Molten Salt Reactor Source Term Generation and Shielding Analyses
(Informa UK Limited, 2023-07-24) Carberry, Kyle; Petrovic, Bojan
The research presented herein outlines a comprehensive process for characterizing the major radiological source terms necessary for radiation protection and licensing activities that one would expect in a liquid-fueled molten salt reactor. This process leverages organic simulation tools in the SCALE modeling and simulation code suite to provide an “off-the-shelf” solution for shielding assessments of this reactor type. Ultimately, this source development process is applied to a representative molten salt reactor system to assess the impact of ex-core source terms on shielding in varying operating conditions. The results of the analysis determined that while the prompt core source is the major dose contributor outside the radiological shielding, specific ex-core features, such as the primary salt loop components and configuration, can have an appreciable dose impact, and thus must be accounted for.
Unsupervised Machine Learning Approaches to Nuclear Particle Type Classification
(IEEE, 2022) Liebers, Nicholas; Huckelberry, Jacob; Ruiz, Daniel C.; Fobar, David; Chapman, Peter
Historically, nuclear science and radiation detection fields of research used Pulse Shape Discrimination (PSD) to label gamma-ray and neutron interactions. However, PSD’s effectiveness relies greatly on the existence of distinguishable differences in an interaction’s measured pulse shape. In the fields of machine learning and data analytics, clustering algorithms provide ways to group samples with similar features without the need for labels. Clustering gamma-ray and neutron interactions may mitigate PSD’s pitfalls, since clustering methods view the total waveform rather than just the area under the tail and the total area under the pulse. However, traditional clustering methods, such as the k-means clustering algorithm, suffer from poor performance on high dimensional data. This study explores unsupervised machine learning methods using Deep Neural Networks (DNN) to cluster gamma-ray and neutron interaction measurements collected with an organic scintillation detector, in order to perform binary labeling of gamma-rays and neutrons. Using various network architectures, this research demonstrates the effectiveness of using autoencoder-based neural networks to cluster gamma-ray and neutron interactions when compared to shallow clustering algorithms. The results reveal the effectiveness of autoencoders on high energy gamma-ray and neutron pulses with an energy deposit greater than 0.80 MeVee whilst greatly outperforming k-means comparatively in all cases.
Unmanned Aircraft System Swarm for Radiological and Imagery Data Collection
(Aerospace Research Central, 2019) Kopeikin, Andrew; Heider, Samuel; Larkin, Dominic; Korpela, Christopher M.; Morales, Ricardo; Bluman, James E.
The purpose of this project is to develop a muti-unmanned aerial system (UAS) to aid in forensic collection and analysis efforts in a post-nuclear blast, by providing a radiation gradient heat-map and live video overwatch of the area...
The use of tabletop exercises in nuclear security education
(Journal of Applied Research in Higher Education, 2018) Shattan, Michael; Seybert, Adam; Gilbreath, Robert B.; Dahunsi, Stephen; Hall, Howard L.
The purpose of this paper is to explore the role of tabletop exercises (TTXs) in graduate nuclear security education, their effectiveness and their relationship to traditional forms of classroom instruction. The paper highlights both the benefits and challenges of TTX implementation—the former including higher student motivation and material retention, and the latter including motivational shifts toward “winning” and possible student exclusionary behavior.
Streamlining Experiment Projections For Resolute Bay Incoherent Scatter Radar (risr) To Facilitate Research Of Space Weather Driven Global Positioning System Scintillations
(IEEE, 2021) Hoxeng, Adam; Loucks, Diana; Wright, William C.; Oxendine, Christopher
Global navigation satellite system (GNSS) signals are used throughout the world for position, navigation, and timing. As these signals travel to Earth's surface from Medium Earth Orbit they can encounter ionospheric scintillation caused by ionospheric plasma irregularities, especially at high latitudes. This ionospheric scintillation is detrimental to GNSS signal strength, accuracy, and confidence. Incoherent scatter radars like Resolute Bay Incoherent Scatter Radar (RISR) are excellent tools for measuring the ionospheric conditions that surround the path of incoming GNSS signals. This paper outlines a process that utilizes Systems Toolkit (STK) and Matrix Laboratory (MATLAB) to streamline the process of identifying valid future conjunctions between GPS signals and RISR radar beams to advance study of the impact of ionospheric scintillations on GNSS signals at high latitudes.
Lagrangian and Impedance-Spectroscopy Treatments of Electric Force Microscopy
(APS, 2019) Dwyer, Ryan P.; Harrell, Lee E.; Marohn, John A.
Scanning probe microscopy is often extended beyond simple topographic imaging to study electrical forces and sample properties, with the most widely used experiment being frequency-modulated Kelvin probe force microscopy. The equations commonly used to interpret this frequency-modulated experiment, however, rely on two hidden assumptions. The first assumption is that the tip charge oscillates in phase with the cantilever motion to keep the tip voltage constant. The second assumption is that any changes in the tip-sample interaction happen slowly. Starting from an electromechanical model of the cantilever-sample interaction, we use Lagrangian mechanics to derive coupled equations of motion for the cantilever position and charge. We solve these equations analytically using perturbation theory, and, for verification, numerically. This general approach rigorously describes scanned probe experiments even in the case when the usual assumptions of fast tip charging and slowly changing samples properties are violated. We develop a Magnus-expansion approximation to illustrate how abrupt changes in the tip-sample interaction cause abrupt changes in the cantilever amplitude and phase. We show that feedback-free time-resolved electric force microscopy cannot uniquely determine subcycle photocapacitance dynamics. We then use first-order perturbation theory to relate cantilever frequency shift and dissipation to the sample impedance even when the tip charge oscillates out of phase with the cantilever motion. Analogous to the treatment of impedance spectroscopy in electrochemistry, we apply this approximation to determine the cantilever frequency shift and dissipation for an arbitrary sample impedance in both local dielectric spectroscopy and broadband local dielectric spectroscopy experiments. The general approaches that we develop provide a path forward for rigorously modeling the coupled motion of the cantilever position and charge in the wide range of electrical scanned probe microscopy experiments where the hidden assumptions of the conventional equations are violated or inapplicable.
Beam propagation study of on-wafer multiple-defect photonic crystal VCSEL arrays
(SPIE, 2021) Stephens, Stone K.; Ingold, Kirk A.; Pfenning, Michael J.; Raftery, James J.; Choquette, Kent D.; Chun Lei
Vertical-emitting laser arrays operating in a coherently coupled regime offer the potential for high brightness and low power. Previous work focused on quantifying the beam propagation factor of on-wafer single-emitter photonic crystal vertical cavity surface emitting laser (PCSEL) devices for comparison with typical measured values of the spectral linewidth and side-mode suppression ratio. Expanding on this work, here we report on a novel method of characterizing the beam propagation factor for 2x1 multiple-defect coherently coupled PCSEL arrays. First, the on-wafer 2x1 PCSEL arrays were characterized to determine the range of injection currents that produced a coherently or incoherently coupled output using a 3-D power map. Both operating regions are explored here. After measuring the spectrum, the beam profiles were captured using a vertically mounted beam profiling system. Each individual laser in the 2x1 array was first operated and characterized independently. The device was then characterized operating in an incoherent and coherent coupled mode, respectively. The beam propagation factor, or M2, was calculated for each set of data using a weighted least-squares curve fit and in accordance with the ISO Standard 11146. As expected, the individual lasers making up the 2x1 array produced near-Gaussian beam profile with M2 close to 1. With both laser elements operating, regardless of the state of coherence, the output beam adopted an asymmetry and the M2 value increased predominately on the lateral axis. In this effort, a parametric study of the beam propagation factor of devices emitting near 850nm is presented.
Designing and Flight-Testing a Swarm of Small UAS to Assist Post-Nuclear Blast Forensics
(IEEE, 2020) Kopeikin, Andrew; Russell, Conner; Trainor, Hayden; Rivera, Ashley; Jones, Tyrus; Baumgartner, Benjamin M.; Manjunath, Pratheek; Heider, Samuel; Surdu, Thomas; Galea, Matthew
Nuclear blasts leave plumes of residue and sources of radiation behind that can be used to determine their origin. To assist in locating the best areas to collect ground samples a team from West Point developed an autonomous Unmanned Aircraft System (UAS) swarm system for expedited remote sensing. The system includes several distributed control algorithms to enable a team of quadrotor UAS to rapidly survey regions for radiation. Each vehicle is equipped with a sensor suite to measure radiation and estimate the source strength on the surface. While UAS survey the area, their data is fused into a single heatmap which becomes available in real-time to end-users employing the Android Tactical Assault Kit (ATAK). A rapid deployment system was developed to streamline how UAS are configured in preflight and enables them to launch directly from a box used for transport. The system was successfully demonstrated in a live flight test event over an active radiation field at Savannah River Site SC in April 2019.
Unsupervised Machine Learning Approaches to Nuclear Particle Type Classification
(IEEE, 2022) Ruiz, Daniel C.; Liebers, Nicholas; Huckelberry, Jacob; Fobar, David; Chapman, Peter
Abstract—Historically, nuclear science and radiation detection fields of research used Pulse Shape Discrimination (PSD) to label gamma-ray and neutron interactions. However, PSD’s effectiveness relies greatly on the existence of distinguishable differences in an interaction’s measured pulse shape. In the fields of machine learning and data analytics, clustering algorithms provide ways to group samples with similar features without the need for labels. Clustering gamma-ray and neutron interactions may mitigate PSD’s pitfalls, since clustering methods view the total waveform rather than just the area under the tail and the total area under the pulse. However, traditional clustering methods, such as the k-means clustering algorithm, suffer from poor performance on high dimensional data. This study explores unsupervised machine learning methods using Deep Neural Networks (DNN) to cluster gamma-ray and neutron interaction measurements collected with an organic scintillation detector, in order to perform binary labeling of gamma-rays and neutrons. Using various network architectures, this research demonstrates the effectiveness of using autoencoder-based neural networks to cluster gamma-ray and neutron interactions when compared to shallow clustering algorithms. The results reveal the effectiveness of autoencoders on high energy gamma-ray and neutron pulses with an energy deposit greater than 0.80 MeVee whilst greatly outperforming k-means comparatively in all cases.
The STEM Faculty Experience at West Point
(Journal of College Science Teaching, 2022) Koleci, Carolann; Kowalski, Eileen M.; McDonald, Kenneth J.
At conferences or meetings, West Point faculty are often asked, “What’s it like to teach at West Point?” Previously, we reported on this question within the context of the cadet’s West Point experience and how STEM courses and opportunities are integrated. Now we turn our focus to the West Point faculty and their unique position of both educating cadets in a traditional sense and helping with the cadets’ character development. In this article, we discuss who the West Point faculty are; what is expected of each faculty member; and how faculty members within chemistry, physics, mechanical engineering, and civil engineering educate and develop future leaders of character for the U.S. Army.
STEM Education Within the West Point Experience
(Journal of College Science Teaching, 2021) Koleci, Carolann; Kowalski, Eileen M.; McDonald, Kenneth J.
At conferences or meetings, West Point faculty are often asked, “What’s it like to teach at West Point?” To answer this question we present the unique model that West Point uses to bridge traditional higher education and the United States Army. The West Point model stems from its mission to develop cadets as leaders of character who are prepared to be the future leaders of the U.S. Army. To fulfill the mission, cadets meet physical and military requirements, in addition to earning a Bachelor of Science degree. Here we discuss how the West Point student body, curriculum, and mission affect courses and opportunities in STEM.
Spin Stabilization of an Air Ambulance Litter
(ASME, 2020) Forden, Christopher; Trinidad, Yanuel; Von Chance-Stutler, Ryan; Bellocchio, Andrew; Bluman, James E.
This paper proposes a new approach to stabilize the spin of a suspended litter during air ambulance rescue hoist operations. Complex forces generated by the helicopter’s downwash may cause a patient suspended in a rescue litter to spin violently. In severe cases, the spin destabilizes the suspended load, risks injury to the patient, and jeopardizes the safety of the aircrew. The presented design employs an anti-torque device to arrest the spin that is safer and faster than a tagline and is without the tactical constraints of the tagline. The device follows tailored control laws to accelerate a flywheel attached to the litter, thereby generating sufficient angular momentum to counteract the spin and stabilize the suspended litter. An inertial measurement unit (IMU) measures the position, angular velocity, and angular acceleration of the litter and delivers this information to a microcontroller. The research and prototype design were developed under the support of the U.S. Army 160th Special Operations Aviation Regiment (SOAR).
Error Reduction for the Determination of Transverse Moduli of Single-Strand Carbon Fibers via Atomic Force Microscopy
(AIR FORCE INSTITUTE OF TECHNOLOGY, 2021) Frey, Joshua D.
PeakForce Atomic Force Microscopy (AFM) Quantitative Nanomechanical Measurement (QNM) is utilized to measure the transverse ﬁber modulus of single strand carbon ﬁbers to less than 5% error for eleven types of carbon ﬁbers, manufactured by Mitsubishi, Toray, and HEXCEL, with longitudinal moduli between 924-231 GPA, including export-controlled ﬁbers. A positive linear correlation between the longitudinal and transverse modulus with an R2=0.76 is found. Statistical and physical criterion for outlier removal are studied and established to improve the quality of data to exclude outlier measurement points in an image based on the peak force, adhesion force, and indentation depth. Statistical and physical criterion are also developed to exclude outlier images within the sample set. Three alternative methods for calculating the transverse modulus using the raw instrument data were studied. The ﬁrst method approximated the indentation force curve using the peak force and adhesion force values. This method calculated moduli lower than that reported by the instrument and with no correlation between the transverse and longitudinal modulus. The second method approximated the indentation force curve using the peak force and net force zero point. This method found values larger than that reported by the instrument and no correlation between the transverse and longitudinal modulus. The ﬁnal method performs a linear ﬁt to the measured indentation force curves at each indentation point. This method also found values lower than reported by the instrument. Pitch-based ﬁbers are found to exhibit lower measurement error than PAN-based ﬁbers. Additionally, PAN ﬁbers exhibited no apparent modulus correlation when the Pitch ﬁbers are excluded. Underlying reasons for this lack of correlation are explored, with the most likely reasons being the diﬀerence in long-range order in the ﬁber microstructure and aging eﬀects due to the diﬀerent sourcing and storage methods used for the PAN ﬁbers. Low uncertainty characterization of the transverse modulus supports greater understanding of ﬁber mechanical behavior and would allow ﬁber manufacturers to certify their ﬁbers in both the longitudinal and transverse axes. Additionally, it would improve the conﬁdence in engineering estimates used by industry and defense programs for transverse performance of carbon ﬁber-reinforced composites.
Development of Nuclear UnderGround Engineered Test Surrogates for Technical Nuclear Forensics Exploitation
(University of Tennessee, 2017) Gilbreath, Robert B.
A method for formulation and production of Nuclear UnderGround Engineered Test Surrogates (NUGETS) based on notional improvised nuclear device (IND) detonations in an underground environment analogous to the Nevada National Security Site (NNSS) is presented. Extensive statistical analyses of precursory geochemical and geophysical characteristics are combined with an augmented surrogate debris cooling technique and predictive IND contributions from the ORIGEN Fallout Analysis Tool. Precursory and resultant elemental compositions, cooling curve calculations, and visual comparison of NUGETS to genuine underground debris are reported. Application of NUGETS methodology to future studies in urban, underground post-detonation technical nuclear forensic (TNF) analysis is suggested.
Applying Machine Learning to Neutron-Gamma Ray Discrimination from Scintillator Readout Using Wavelength Shifting Fibers
(Journal of Radiation Effects: Research and Engineering, 2021) Moore, Michael K.; Scherrer, M H.; Clement, Sean; Hawthorne, Daniel S.; Ruiz, Daniel C.
Advances in machine learning have found wide applications including radiation detection. In this work, machine learning is applied to neutron-gamma ray discrimination of an organic liquid scintillator (OLS) readout using wavelength shifting (WLS) fibers. The objective of using WLS fiber is to enable the transfer of the light signal from the scintillation medium, with almost any active volume geometry, to a low-profile photomultiplier. This is a common practice in high-energy physics research and has proven to be very effective for such applications. The drawback of this approach is the light pulses carried to the photomultiplier through the WLS fibers do not perfectly replicate the original OLS light pulses’ intensities or timing. This drawback causes traditional pulse shape discrimination algorithms applied to the degraded light pulses to fail to discriminate between neutron and gamma ray events. However, differences in the degraded light pulses for neutrons and gamma rays still exist and various machine learning algorithms can be applied to identify these differences. An experimental system was constructed to simultaneously capture part of the scintillation medium signal and the corresponding signal through the WLS fibers. Using the known neutron-gamma ray discrimination characteristics directly measured in the scintillation medium to provide the ground truth, supervised machine learning algorithms were applied to the corresponding light pulses carried to the photomultiplier through the WLS fibers. The results indicate that this approach will en-able enhanced recovery of neutron-gamma ray discrimination information. This research effort will focus on two aspects of the OLS-WLS system: 1) developing an experimental system to create machine learning training data and 2) applying and evaluating various machine learning algorithms.
Analysis of the Optical Coupling of Wavelength-Shifting Fibers to Organic Liquid Scintillator Filled Fluoropolymer Tubes for Industrial and Nuclear Security Applications
(Massachusetts Institute of Technology, 2014) Schools, Chad C.
Industrial and nuclear security applications continue to push radiation detection development into new and exciting frontiers. In this work, an innovative detection module is developed and tested for use in a cosmic ray imaging (CRI) system designed for oil field characterization and is evaluated for its potential use in a fast neutron detection system for nuclear security applications. By measuring density changes in the reservoir, the CRI system will provide real-time information about steam chamber development during the enhanced oil field recovery process known as steam assisted gravity drainage (SAGD). The ability to monitor the development of the steam chamber region has the potential to provide important information, which could be used to optimize the growth and uniformity of the underground steam chamber and minimize costs. The organic liquid scintillator based detection modules also detect fast neutrons. During the initial characterization of an unidentified radioactive source, it is important to have the capability to determine if special nuclear material (SNM) is present and if it is configured to produce a nuclear yield. The emission of multiple neutrons during a single fission makes it possible to use this unique timing characteristic to identify SNM. The number of specialists trained to handle nuclear devices is limited making this determination a critical step in properly responding to the situation. The detector module consists of a 5 mm diameter by 2-meter long fluoropolymer tube filled with organic liquid scintillator (OLS), optically read-out using wavelength shifting (WLS) fibers. The 1:400 ratio of diameter to length makes light collection from the organic scintillator very challenging. Over ten configurations of OLS, fluoropolymer tubes, and WLS fibers were tested. The final configuration consisted of two 2mm BCF-91A WLS fibers optically coupled to the outside of an optically transparent fluorinated ethylene propylene (FEP) tube filled with a commercial OLS (EJ-309). Cosmic ray muons produce large light pulses in the OLS of which a portion reaches the external WLS fibers. The WLS fibers re-emits the light at longer wavelengths and acts as a multi-mode light guide channeling the signal to photomultiplier tubes located at each end of the WLS fibers. This module demonstrated excellent detection efficiency with less than 5% signal reduction, at any point along the module, due to optical attenuation. Timing analysis of the WLS fiber signals also provided coarse position determination, 40 cm, which opens design options not previously available. An important characteristic required of neutron detectors for nuclear security applications is the ability to discriminate fast neutron and gamma ray events. Initial tests have demonstrated the capability of our module to discriminate neutron and gamma rays by applying the rise time pulse shape discrimination (PSD) method to the WLS fiber signals. EJ-309 is well known for its PSD capabilities. Coupling this desirable characteristic with loss free, low attenuation optical read-out through a WLS fiber has the potential to broaden significantly liquid scintillator applications.

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