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The Evolution Of Reliability Methods For Nondestructive Evaluation (Nde): From Probability Of Detection (Pod) Conception To Contemporary Practices, Christine E. Knott, Jennifer Brown, John Aldrin, Christine M. Schubert Kabban
The Evolution Of Reliability Methods For Nondestructive Evaluation (Nde): From Probability Of Detection (Pod) Conception To Contemporary Practices, Christine E. Knott, Jennifer Brown, John Aldrin, Christine M. Schubert Kabban
Faculty Publications
Nondestructive evaluation (NDE) methods are powerful tools for detecting and characterizing flaws in structural components, but their reliability must be evaluated before they can be used in critical applications. For more than 50 years, probabilistic and statistical methods have been used effectively to estimate reliability by describing an NDE system’s Probability of Detection (POD) for flaws of realistic sizes. The POD methods used by the USAF and NASA, like Hit/Miss, Signal-Response (â vs. a), and Point Estimate method (PEM, a.k.a. 29/29) have evolved, alongside newer approaches like Limited Sample POD (LS-POD) method, and Model Assisted Probability of Detection …
Karhunen-Loève Modes For Coherent Arrays, Jack E. Mccrae, Santasri R. Bose-Pillai, Steven T. Fiorino
Karhunen-Loève Modes For Coherent Arrays, Jack E. Mccrae, Santasri R. Bose-Pillai, Steven T. Fiorino
Faculty Publications
The optimal modes for correcting atmospheric turbulence on coherent arrays are determined. These Karhunen-Loève modes are eigenvectors of a covariance matrix. Creating this covariance matrix requires knowledge of the power spectrum of the turbulence, the aperture geometry, and a basis set for the matrix. The Kolmogorov power law is ordinarily chosen here for the turbulence spectrum. By choosing the phase on each array subaperture element minus the phase averaged over all subapertures as this basis, infinite values in the variances and covariances can be avoided. The piston mode of the whole array, which would otherwise also be infinite, is thus …
Evaluating A Dual-Beacon Hartmann Turbulence Profiling Technique Using Wave Optics Simulations, Benjamin C. Wilson, Matthew Kalensky, Santasri Bose-Pillai, Jack E. Mccrae
Evaluating A Dual-Beacon Hartmann Turbulence Profiling Technique Using Wave Optics Simulations, Benjamin C. Wilson, Matthew Kalensky, Santasri Bose-Pillai, Jack E. Mccrae
Faculty Publications
Resolving how optical turbulence varies along a propagation path remains a key challenge for designers of free-space optical propagation systems. Instruments such as scintillometers and differential image motion monitors are commonly used, but only provide path-integrated turbulence estimates. Point sensors provide localized estimates of turbulence strength and can be used to generate path-resolved profiles when an array of point sensors are distributed along the optical path. However, this approach can be costly and complex to deploy in certain environments. Alternatively, a single point sensor can be mounted on a mobile platform that collects data while traversing the optical path, although …
Development Of Robust Ratio Linear Fitting Method Of Temperature And Emissivity Separation For High-Temperature Data, Mitchell Manzardo, Michael L. Dexter, Shannon R. Young, John Bowlan, Anthony L. Franz
Development Of Robust Ratio Linear Fitting Method Of Temperature And Emissivity Separation For High-Temperature Data, Mitchell Manzardo, Michael L. Dexter, Shannon R. Young, John Bowlan, Anthony L. Franz
Faculty Publications
Accurate temperature and emissivity separation from thermal infrared radiance is essential for characterizing materials under high-temperature laboratory conditions. Existing temperature and emissivity separation methods have largely been developed for multispectral remote sensing applications, where long atmospheric path lengths require extensive atmospheric compensation. In contrast, the current work considers hyperspectral laboratory measurements acquired over a short optical path, where atmospheric effects are comparatively small but increased measurement uncertainty remains within portions of the measured spectrum. The ABB MR304 FTIR spectrometer used in this study exhibits reduced optical transmission below approximately 2.5 μm, producing increased measurement uncertainty within the spectral region containing …
Effective Visibility In The Infrared Bands, Peter L. Dean-Erlander, Steven T. Fiorino, Ronald G. Driggers
Effective Visibility In The Infrared Bands, Peter L. Dean-Erlander, Steven T. Fiorino, Ronald G. Driggers
Faculty Publications
Visibility is an atmospheric metric for the comparison of terrestrial imaging conditions and locations. A limitation of visibility is that it is defined for the visible spectrum only, and there is no simple infrared equivalent. This study compares three reflective infrared wavebands: near-IR (NIR), shortwave IR (SWIR), and extended shortwave IR (eSWIR), to the visible band across a global set of cities to develop three rule-of-thumb functions for IR effective visibility. To accomplish this, a radiometric sensor model is combined with the Laser Environmental Effects Definition and Reference (LEEDR) software package to calculate the visibility of a black-and-white contrast target …
An Atmospheric Optical Turbulence Structure Parameter Measurements System Based On Direct Ri Sensing Using High-Resolution Fiber-Optic Sensors, Matej Njegovec, Simon Pevec, Vedran Budinski, Boris Macuh, Melissa K. Beason, Denis Onlagic
An Atmospheric Optical Turbulence Structure Parameter Measurements System Based On Direct Ri Sensing Using High-Resolution Fiber-Optic Sensors, Matej Njegovec, Simon Pevec, Vedran Budinski, Boris Macuh, Melissa K. Beason, Denis Onlagic
Faculty Publications
The paper presents a method to characterize the refractive index structure parameter (Cn2) associated with optical turbulence directly. The characterization system is based on miniature, high‑resolution, all‑fiber refractive index (RI) sensors. The refractive index sensors employ open‑path, low‑finesse Fabry–Perot interferometers that are approximately 8 mm long and 200 μm in diameter. The active part of the interferometers is made of ultra‑low‑expansion glass, which eliminates the influence of thermal expansion on the refractive index measurements. The proposed refractive index sensor, operating in a differential configuration, achieved a resolution of 2×10-9 RIU using a custom‑designed spectral interrogation system …
The Use Of Machine Learning Models For Predicting The Dielectric Strength Of Gases, Matthew Mileski, Paul W. Groth, Timothy S. Wolfe, Adib J. Samin
The Use Of Machine Learning Models For Predicting The Dielectric Strength Of Gases, Matthew Mileski, Paul W. Groth, Timothy S. Wolfe, Adib J. Samin
Faculty Publications
Technological advancements in high voltage systems have pushed sulfur hexafluoride (SF6) to its operational limits. Furthermore, this gas has other drawbacks including a high liquefaction temperature and a high global warming potential. Therefore, there has been an urgent need to find alternative gases with high dielectric strength (DS). In this work, density functional theory (DFT) is used to calculate molecular descriptors that are fed into an artificial neural network (ANN) and a random forest (RF). These machine learning (ML) models are then used to predict the DS for hundreds of molecules. A finite element model (FEM) is also used to …
Enhancing A Mid-Wave Infrared Fourier Transform Hyperspectral Imager For Explosions, James T. Stofel, Kody A. Wilson, Martin Larivière-Bastien, Anthony L. Franz, Michael L. Dexter
Enhancing A Mid-Wave Infrared Fourier Transform Hyperspectral Imager For Explosions, James T. Stofel, Kody A. Wilson, Martin Larivière-Bastien, Anthony L. Franz, Michael L. Dexter
Faculty Publications
Capturing reliable hyperspectral imager data at a meaningful frame rate for explosions and other fast-changing scenes is not possible in the mid-wave infrared region under traditional sensor operating configurations and processing techniques, which typically have frame rates on the order of 0.5–2.0 Hz. To combat these shortcomings, the scene acquisition parameters were tailored for explosions and a new method for processing optical signatures of fast transient scenes with Fourier-transform infrared hyperspectral imagers was developed. For this technique, the instrument was first configured to collect asymmetric interferograms while optimizing the number of measurement points on the short side of the interferogram. …
Modeling Formation Of Turbulent Sporadic-E Clouds Using Realistic Wind Data, Aaron M. Schinder, Kenneth S. Obenberger, Jorge L. Chau, Juan M. Urco, Matthias Clahsen, Benjamin F. Akers, Daniel J. Emmons
Modeling Formation Of Turbulent Sporadic-E Clouds Using Realistic Wind Data, Aaron M. Schinder, Kenneth S. Obenberger, Jorge L. Chau, Juan M. Urco, Matthias Clahsen, Benjamin F. Akers, Daniel J. Emmons
Faculty Publications
A high resolution two-dimensional multi-fluid model of sporadic-E layers was developed and driven with physically realistic mesosphere, lower thermosphere (MLT) winds measured over Albuquerque, New Mexico. The realistic E-region winds are produced by the HYdrodynamic Point-wise Environment Reconstructor (HYPER) model that ingests meteor derived wind observations from a Spread-spectrum Interferometric Multistatic meteor radar Observing Network (SIMONe) system combined with the Navier-Stokes equations to provide high resolution three-dimensional wind fields over time. Sporadic-E dynamics are simulated using both realistic winds from HYPER as well as idealized hyperbolic tangent windshears to compare and contrast. Overall, the model shows greater inhomogeneity and irregularity …
Observations On Recurrent Loss In The Neural Network Model Of A Partial Differential Equation: The Advection–Diffusion Equation, Jonah A. Reeger
Observations On Recurrent Loss In The Neural Network Model Of A Partial Differential Equation: The Advection–Diffusion Equation, Jonah A. Reeger
Faculty Publications
A growing body of literature has been leveraging techniques of machine learning (ML) to build novel approaches to approximating the solutions to partial differential equations. Noticeably absent from the literature is a systematic exploration of the stability of the solutions generated by these ML approaches. Here, a recurrent network is introduced that matches precisely the evaluation of a multi-step method paired with a collocation method for approximating spatial derivatives in the advection–diffusion equation. This allows for two things: (1) the use of traditional tools for analyzing the stability of a numerical method for solving PDEs and (2) bringing to bear …
Generation Of Khz-Rate Complex-Structured Liquid Targets For Relativistic Laser–Plasma Interactions, Michael L. Dexter, Stephen J. Hageman, Gregory Ngirmang, Kyle D. Frische, Joseph Snyder, John T. Morrison, Enam A. Chowdury, Anil K. Patnaik
Generation Of Khz-Rate Complex-Structured Liquid Targets For Relativistic Laser–Plasma Interactions, Michael L. Dexter, Stephen J. Hageman, Gregory Ngirmang, Kyle D. Frische, Joseph Snyder, John T. Morrison, Enam A. Chowdury, Anil K. Patnaik
Faculty Publications
With the rise of high repetition rate ultra-intense laser systems, there is a need for solid density targets to study relativistic laser–plasma interactions that can operate at the same repetition rate. Flowing liquid targets are attractive because they are self-replenished, debris free, cost effective and easy to use. Liquid targets have been used for high-repetition rate (up to kHz rate) generation of electrons, protons, x rays, and neutrons by our group and elsewhere. In this Letter, we demonstrate a kHz-rate generation of a variety of dynamically shaped complex-structured targets from the interaction of a 1016 W/cm2 focused laser …
Analysis And Machine Learning Adaptation Of A Cognitive Model For Human Memory, Trevor Cross, Aihua W. Wood
Analysis And Machine Learning Adaptation Of A Cognitive Model For Human Memory, Trevor Cross, Aihua W. Wood
Faculty Publications
In this paper, we use the Duolingo SLAM dataset to analyze several cognitive models of second language acquisition and develop new approaches for enhanced performance. In particular, we consider the Predictive Performance Equation and some of its underlying power laws. Leveraging insights from machine learning, we develop simple one-feature models as building blocks for combined models that match or in certain cases outperform the existing models at much reduced computational cost. In addition, a neural network with one fully connected hidden layer is constructed that outperforms all other models on sufficiently large datasets.
Boiling Flow Estimation For Aero-Optic Phase Screen Generation, Jeffrey W. Utley, Gregery T. Buzzard, Charles A. Bouman, Matthew R. Kemnetz
Boiling Flow Estimation For Aero-Optic Phase Screen Generation, Jeffrey W. Utley, Gregery T. Buzzard, Charles A. Bouman, Matthew R. Kemnetz
Faculty Publications
Aero-optic effects due to turbulence can reduce the effectiveness of transmitting light waves to a distant target. Methods to compensate for turbulence typically rely on realistic turbulence data, which can be generated by i) experiment, ii) high-fidelity computational fluid dynamics (CFD), iii) low-fidelity CFD, and iv) autoregressive methods. However, each of these methods has significant drawbacks, including monetary and/or computational expense, limited quantity, inaccurate statistics, and overall complexity. By contrast, the boiling flow algorithm is a simple, computationally efficient model that can generate atmospheric phase screen data with only a handful of parameters. However, boiling flow has not been widely …
Active Learning Of Constraint Boundaries Using Expected Magnitude Of Incorrectness And Neural Networks, Atticus Beachy, Ramana V. Grandhi
Active Learning Of Constraint Boundaries Using Expected Magnitude Of Incorrectness And Neural Networks, Atticus Beachy, Ramana V. Grandhi
Faculty Publications
This research proposes an acquisition function for constraint boundary identification, with applications to hypersonic air vehicles. Hypersonic vehicles endure extreme thermal loads caused by aerodynamic heating, resulting in a strong coupling between structural performance and aerothermodynamics. However, modeling coupled system behaviors requires simultaneous consideration of both aerodynamic and structural design variables, increasing the dimensionality of the design trade space and the difficulty of accurately modeling the constraints. Several active learning schemes have been proposed to accelerate identification of the composite feasible region that satisfies all constraints. Some of these require integrating the surrogate model over the entire design space with …
Matching Two Long Interferometric Pathlengths Using Low Temporal Coherence Light For Finding Hong–Ou–Mandel Dip, Keith A. Wyman, Noah S. Everett, Anil K. Patnaik
Matching Two Long Interferometric Pathlengths Using Low Temporal Coherence Light For Finding Hong–Ou–Mandel Dip, Keith A. Wyman, Noah S. Everett, Anil K. Patnaik
Faculty Publications
Hong–Ou–Mandel (HOM) dip from a biphoton source in a two-photon interferometer provides a myriad of quantum tools for quantum communication and sensing. But the stringent requirements for spatial coherence between the photon pair makes it prohibitively difficult to observe high-fidelity HOM dip in long-distance free-space implementations, e.g., for the photon pairs involved in quantum communication need to match the two path lengths within a few 10 s of micron because of the short coherence width of the two-photon wave-packet. While many techniques for the pathlength balancing of two interferometric arms have been studied and applied extensively, such balancing is further …
Winddensity-Mbir: Model-Based Iterative Reconstruction For Wind Tunnel 3d Density Estimation, Karl J. Weisenburger, Gregery T. Buzzard, Charles A. Bouman, Matthew R. Kemnetz
Winddensity-Mbir: Model-Based Iterative Reconstruction For Wind Tunnel 3d Density Estimation, Karl J. Weisenburger, Gregery T. Buzzard, Charles A. Bouman, Matthew R. Kemnetz
Faculty Publications
Experimentalists often use wind tunnels to study aerodynamic turbulence, but most wind tunnel imaging techniques are limited in their ability to take non-invasive three-dimensional (3D) density measurements of turbulence. Wavefront tomography is a technique that uses multiple wavefront measurements from various viewing angles to non-invasively measure the 3D density field of a turbulent medium. Existing methods make strong assumptions, such as a spline basis representation, to address the ill-conditioned nature of this problem. We formulate this problem as a Bayesian, sparse-view tomographic reconstruction problem and develop a model-based iterative reconstruction algorithm for measuring the volumetric 3D density field inside a …
Does Patient History Influence Capsular Contracture? An Exploratory Analysis With Machine Learning, Thomas M. Johnstone, Daniel Najafali, Jennifer K. Shaw, Justin M. Camacho, Chancellor Johnstone, Rahim S. Nazerali, Gordon K. Lee
Does Patient History Influence Capsular Contracture? An Exploratory Analysis With Machine Learning, Thomas M. Johnstone, Daniel Najafali, Jennifer K. Shaw, Justin M. Camacho, Chancellor Johnstone, Rahim S. Nazerali, Gordon K. Lee
Faculty Publications
Background: Capsular contracture (CC) is a frequent and distressing complication of breast augmentation and reconstruction. Although numerous patient-, surgical-, and implant-related risk factors have been proposed, reliable population-level predictors remain inconsistent across studies. This study evaluates whether administrative medical history, as encoded by ICD and CPT codes, contains sufficient predictive signal to identify patients at risk for CC using machine learning. Methods: Patients were queried from the MerativeTM MarketScan® Research Databases from 2003 to 2017 with CPT codes for implant-based breast reconstruction and augmentation. ICD codes were then used to identify all events and conditions of a patient’s history. Hyperparameter-tuned …
Inconsistencies Emerge Between Regional And Local-Scale Water Security Metrics At Military Installations, Abigail Birnbaum, Michael L. Berg, Caitlin Grady, Daniel J. Weeks, Christopher M. Chini
Inconsistencies Emerge Between Regional And Local-Scale Water Security Metrics At Military Installations, Abigail Birnbaum, Michael L. Berg, Caitlin Grady, Daniel J. Weeks, Christopher M. Chini
Faculty Publications
Recent United States federal policy for military installations has emphasized the importance of developing a standardized approach for water security assessment to monitor changes in water resources and the ability for an installation to meet both its civilian and mission needs. For military installations in the United States, these assessments must consider demands both inside and outside the installation’s fence line, as regional resources are required to meet mission readiness. Focusing on physical water scarcity, this study compares four water security metrics with unique formulations and spatial resolutions, including an installation-scale metric and multiple regional metrics defined for either baseline …
Measurements And Scaling Of Ion Propulsion Impulse During Driven Magnetic Reconnection, Fatima Ebrahimi, Nicholas A. O'Gorman, Kush Maheshwari, Jongsoo Yoo, Alexandre Sainterme, Hantao Ji
Measurements And Scaling Of Ion Propulsion Impulse During Driven Magnetic Reconnection, Fatima Ebrahimi, Nicholas A. O'Gorman, Kush Maheshwari, Jongsoo Yoo, Alexandre Sainterme, Hantao Ji
Faculty Publications
Impulse scaling during magnetic reconnection, the magnetic energy conversion to kinetic energy, via direct Mach probe measurements in Magnetic Reconnection Experiment is examined. Ion exhaust velocity and impulse scalings with reconnecting magnetic field during the push phase of driven reconnection are presented. The outflows and impulse measurements are compared with global MHD simulations. Both measurements and simulations reveal a favorable scaling, greater than linear, of impulse with reconnecting field. These scaling results establish that magnetic reconnection could be utilized for plasma propulsion.
Sheets Of Spectral Data Of Stokes Waves In Weakly Nonlinear Models, Benjamin F. Akers, Ryan Creedon
Sheets Of Spectral Data Of Stokes Waves In Weakly Nonlinear Models, Benjamin F. Akers, Ryan Creedon
Faculty Publications
We study the spectral stability of small-amplitude Stokes waves in a family of weakly nonlinear, unidirectional models of the form ut + Lu + (u2)x = 0. We introduce a perturbation method to expand the spectral data in wave amplitude near flat-state eigenvalue collisions, with the ratio of the colliding modes as a free parameter. This yields sheets of spectral data whose slices at fixed amplitude give isolas of instability. The same perturbation framework treats both high-frequency and Benjamin--Feir instabilities, extends to discontinuous dispersion relations (including the Akers--Milewski equation), and, for the first time, provides an …
Impact Of Collodion Thickness On Fission Track Generation Within Lexan Employed For Nuclear Forensic Analyses, Ryan K. Chapman, Zachary P. Meisel, Abigail A. Bickley
Impact Of Collodion Thickness On Fission Track Generation Within Lexan Employed For Nuclear Forensic Analyses, Ryan K. Chapman, Zachary P. Meisel, Abigail A. Bickley
Faculty Publications
We report results from a computational study of fission track damage within a Lexan substrate caused by 235U suspended within a collodion film of varied thickness. MCNP6.3 was used to model energy deposition and displacements per atom within a Lexan film coated by a film of collodion, located within the air-filled 6 inch dry tube of a TRIGA reactor. The collodion layer was varied between 20 and 100 μm thickness, resulting in an 8.8% difference in energy deposition and 12% difference in displacements-per-atom within the Lexan substrate. These findings indicate that collodion adhesive thickness has a moderate impact on …
Comparison Of Anomaly Detection Methods On Event-Based Vision Sensor Data In A High Noise Environment, Will Johnston, Anthony L. Franz, Shannon R. Young, Rachel Oliver, Zachary Theis, Brian Mcreynolds, Michael L. Dexter
Comparison Of Anomaly Detection Methods On Event-Based Vision Sensor Data In A High Noise Environment, Will Johnston, Anthony L. Franz, Shannon R. Young, Rachel Oliver, Zachary Theis, Brian Mcreynolds, Michael L. Dexter
Faculty Publications
Event-based vision sensors (EVSs) provide unique frequency analysis opportunities due to their event data output and high temporal resolution. Anomaly detection methods used in hyperspectral analysis can be used on the event frequency spectra to detect targets. However, the introduction of a strong, flickering interfering source can reduce the EVS sensitivity and obscure targets of interest. Previous work presented a method showing that targets could still be detected through an overwhelming source using frequency analysis, background suppression, and statistical filtering. This paper extends that research and compares the ability of five different eigenanalysis anomaly detection methods (principal component background suppression …
Radiation Protection Factor Research: The Keystone For Conventional-Nuclear Integration (Cni), Andrew W. Decker
Radiation Protection Factor Research: The Keystone For Conventional-Nuclear Integration (Cni), Andrew W. Decker
Faculty Publications
This article briefly describes Defense Threat Reduction Agency (DTRA) vehicle Radiation Protection Factor (RPF) research and explains its critical role in enabling future Operations in a Nuclear Environment (ONE) and Conventional-Nuclear Integration (CNI) on behalf of the US Department of War (DoW).1 As such, the background and practical utility of RPF values is discussed, as well as the justification for renewed Army and DoW investment into RPF research to enhance Joint military planning, survivability, and lethality on tomorrow’s nuclear battlefields. Advances in DTRA RPF research directly strengthen US strategic and extended deterrence efforts and support all Agencies and Departments responsible …
Natural Convection Of Cuo-Water Nanofluid Flow Along A Vertical Plate With Variable Thermophysical Properties And Discrete Heat Sources, Nepal Roy, Ioan Pop, Rama Subba Reddy Gorla
Natural Convection Of Cuo-Water Nanofluid Flow Along A Vertical Plate With Variable Thermophysical Properties And Discrete Heat Sources, Nepal Roy, Ioan Pop, Rama Subba Reddy Gorla
Faculty Publications
Effects of discrete heat sources along a vertical plate are of practical importance due their occurrence in electronic devices. For growing demand of electronic appliances and their advancement, cooling processes of them must be improved. As usual fluids have limited heat transfer, nanofluids made by dispersing nanoparticles into them are utilized to enhance thermal performance. However, flow characteristics and heat transfer of a nanofluid for discrete heat sources along a vertical plate need to be explored. For this reason, this study analyzes the natural convective heat transfer and flow behaviors of CuO-water nanofluid induced by discrete heat sources along a …
Analogy2kg: An Automatic Pipeline For Deriving Knowledge Graphs From Long-Text Analogies, Kara Combs, Lance E. Champagne, Bruce A. Cox, Christine M. Schubert Kabban, Trevor Bihl, Grace Lemming
Analogy2kg: An Automatic Pipeline For Deriving Knowledge Graphs From Long-Text Analogies, Kara Combs, Lance E. Champagne, Bruce A. Cox, Christine M. Schubert Kabban, Trevor Bihl, Grace Lemming
Faculty Publications
Analogical reasoning is an increasingly popular, lightweight solution to enable large language model (LLM)-level reasoning without computational complexity. Still, it has yet to be adopted due to its reliance on strictly hand-formatted data. Therefore, we propose Analogy2KG (“Analogy to Knowledge Graph”), as an automatic pipeline that transforms text into a KG format via a fine-tuned version of information extraction (IE) algorithms for long-text analogies. The need to verify that the complex underlying analogical structure of the data is maintained was done via paired samples tests in the creation and validation of this pipeline. Graph density was used to evaluate the …
Characterizing Atmospheric Turbulence With The Lunar Step Response Method, Patrick D. Carattini, Caleb J. Stilp, Katelyn M. Atkinson, Stephen C. Cain
Characterizing Atmospheric Turbulence With The Lunar Step Response Method, Patrick D. Carattini, Caleb J. Stilp, Katelyn M. Atkinson, Stephen C. Cain
Faculty Publications
Most methods that astronomers use to characterize the strength of atmospheric turbulence in and around their observatories use differential image motion monitors observing a star to provide the necessary data for the measurement. With the Moon becoming a greater national priority, the need to characterize atmospheric paths between observatories on Earth and the Moon is potentially going to grow in the future. To this end, the differential image motion monitor is not an ideal instrument for characterizing turbulence along paths between observatories and the Moon as the bright Moon makes it difficult to detect and locate stars in its vicinity. …
Transfer Learning Neural Networks For Nuclear Forensic Image Morphology Using Image Splitting Techniques, Niko A. Petrocelli, Lee C. Lambert, Brett J. Borghetti, Abigail A. Bickley
Transfer Learning Neural Networks For Nuclear Forensic Image Morphology Using Image Splitting Techniques, Niko A. Petrocelli, Lee C. Lambert, Brett J. Borghetti, Abigail A. Bickley
Faculty Publications
Manual morphological analysis of actinide particles from scanning electron microscope (SEM) imagery is a critical component of nuclear forensics but is prone to significant inter-analyst variability. To address this challenge, this work develops and evaluates an automated classification method using deep learning. We introduce a methodology based on partitioning 1906 SEM images, representing 13 classes of uranium compounds, into smaller patches for analysis. Three convolutional neural network (CNN) architectures of increasing complexity were compared: a custom baseline CNN, a simple transfer learning model using ResNet50v1, and a complex model featuring hierarchical feature extraction and a spatial attention mechanism built upon …
Dynamic Magnetic Null Behavior In Planar Ion Diodes: Particle-In-Cell Analysis Of Field Oscillations And Ion Beam Dynamics, Jesse C. Foster, Stephen B. Swanekamp, Paul F. Ottinger
Dynamic Magnetic Null Behavior In Planar Ion Diodes: Particle-In-Cell Analysis Of Field Oscillations And Ion Beam Dynamics, Jesse C. Foster, Stephen B. Swanekamp, Paul F. Ottinger
Faculty Publications
Particle-in-cell simulations of a 1.75 MV, 375 kA, and 50 ns planar pinched-beam diode reveal that the strongest gigahertz-frequency oscillations in electric field and ion current arise from the dynamic motion of the magnetic null near the anode tip. These oscillations, which appear when the ion transit time becomes comparable to the local field-variation timescale, periodically expand the effective anode–cathode gap and generate bursts of over-accelerated ions. The resulting ion energy spectrum broadens substantially near the null while maintaining excellent beam uniformity along the anode. The simulations, therefore, demonstrate a direct physical linkage between ion transit time instability and magnetic …
Experiments Towards A Neutron Target For Measurements In Inverse Kinematics, S. F. Dellmann, Caroline M. Harrington, O. R. Cantrell, A. L. Cooper, A. Couture, D, V, Gorelov, I Knapová, S. M. Mosby, R. Reifarth, A. Alvarez, A. Aprahamian, J. Butz, I. J. Bos, Michael T. Febbraro, T. Hankins, B. M. Harvey, T. Heftrich, M. Le, Juan J. Manfredi, A. B. Mcintosh, K. V. Manukyan, M. Matney, S. Regener, D. Robertson, A. Simon, D. Sokolovic, E. Stech, G. Tabacaru, W. Tan, M. Wiescher, S. Yennello
Experiments Towards A Neutron Target For Measurements In Inverse Kinematics, S. F. Dellmann, Caroline M. Harrington, O. R. Cantrell, A. L. Cooper, A. Couture, D, V, Gorelov, I Knapová, S. M. Mosby, R. Reifarth, A. Alvarez, A. Aprahamian, J. Butz, I. J. Bos, Michael T. Febbraro, T. Hankins, B. M. Harvey, T. Heftrich, M. Le, Juan J. Manfredi, A. B. Mcintosh, K. V. Manukyan, M. Matney, S. Regener, D. Robertson, A. Simon, D. Sokolovic, E. Stech, G. Tabacaru, W. Tan, M. Wiescher, S. Yennello
Faculty Publications
Neutron-induced reactions play an important role in fundamental nuclear physics, nuclear astrophysics, and applications. In the case of reactions on rare isotopes, there are limited options for direct experimental measurements. The Neutron Target Demonstrator project at Los Alamos National Laboratory seeks to test the feasibility of moderating spallation neutrons within a 1 m3graphite cube to create a standing neutron target for neutron-induced reaction measurements in inverse kinematics. This paper presents the results of experimental neutron flux distribution tests using neutron sources (ranging from 1 keV to 50 MeV) created by accelerators at the University of Notre Dame and …
Discovery Of Dynamical Structures Mapping Chaotic Transport Pathways In The Earth–Moon Cr3bp, Tyler J. Kapolka, Christina E. Paljug, Robert A. Bettinger, Rachel Oliver, Bruce A. Cox, Jeremiah A. Specht
Discovery Of Dynamical Structures Mapping Chaotic Transport Pathways In The Earth–Moon Cr3bp, Tyler J. Kapolka, Christina E. Paljug, Robert A. Bettinger, Rachel Oliver, Bruce A. Cox, Jeremiah A. Specht
Faculty Publications
Chaos; Deterministic chaos; Earth–Moon system; Poincaré map; Quasi-chaotic; Surface of section /// For the Circular Restricted 3-Body Problem (CR3BP), the topologies present within a Poincaré map enable the extraction of useful information regarding periodic, quasi-periodic, and chaotic trajectory behavior. Aside from the prominent topologies that follow distinct concentric patterns around fixed points, indicative of the periodic and quasi-periodic motion that is often the central focus of CR3BP research, there are also many “dusty” regions on the Poincaré map that appear random without an apparent structure and are indicative of chaotic motion. This paper, for the first time in literature, identifies …