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Articles 91 - 120 of 8785
Full-Text Articles in Entire DC Network
Library Resources @ The D'Azzo Research Library, Sara Craycraft
Library Resources @ The D'Azzo Research Library, Sara Craycraft
AFIT Library Knowledge Base
No abstract provided.
The Spectral Response Of Time-Resolved Piv In A Turbulent Boundary Layer, Peter Manovski, Wagih Abu Rowin, Henry Ng, Paul Gulotta, Matteo Giacobello, Charitha De Silva, Nicholas Hutchins, Ivan Marusic
The Spectral Response Of Time-Resolved Piv In A Turbulent Boundary Layer, Peter Manovski, Wagih Abu Rowin, Henry Ng, Paul Gulotta, Matteo Giacobello, Charitha De Silva, Nicholas Hutchins, Ivan Marusic
Student Publications
This study presents the application of time-resolved particle image velocimetry (TR-PIV) to measure the mean and fluctuating velocity components in a turbulent boundary layer (TBL) over an axisymmetric body of revolution. A narrow wall-normal strip of the flow was captured using a synchronised high-speed laser and camera at a recording frequency of up to 80 kHz. The resulting streamwise and wall-normal velocity TR-PIV data were validated against hot-wire anemometry measurements and direct numerical simulations (DNS) of a flat plate under matched flow conditions. The mean flow results showed good agreement between all methods, while the expected attenuation due to the …
Investigation Of Stability And Control Shortcomings Of The North American X-15, William Lorenzo, Ramana Grandhi, Timothy T. Takahashi
Investigation Of Stability And Control Shortcomings Of The North American X-15, William Lorenzo, Ramana Grandhi, Timothy T. Takahashi
Faculty Publications
There is growing interest in the design of maneuvering high-speed aircraft to fly within or at the edge of the atmosphere. We identify and develop novel quasi-static vehicle screening methodologies, suitable for use during preliminary design, to better predict an incipient loss of control due to the dynamic effects of feedback. We validate these metrics by reverse-engineering Neil Armstrong’s 1962 loss of control and inadvertent atmospheric skip while piloting the X-15. In 1962, then-extant flight dynamics screening methods did not forecast likely troubles. We assemble and refine a collection of predictive metrics which operate upon basic quasi-static aerodynamic data and …
Isotope Production Modeling In Sodium-Cooled Fast Reactors, Aaron W. Burkhardt
Isotope Production Modeling In Sodium-Cooled Fast Reactors, Aaron W. Burkhardt
Theses and Dissertations
The accurate prediction of isotopic compositions in Sodium-Cooled Fast Reactors (SFRs) is essential for nuclear forensic analyses and international nuclear treaty monitoring, particularly with the increased global deployment of Generation-IV reactors. This research developed and validated a detailed computational model tailored specifically to the Prototype Fast Breeder Reactor (PFBR), employing advanced Monte Carlo neutron transport methods, sophisticated burnup modeling, and variance reduction techniques. Validation against empirical data from the Experimental Breeder Reactor-II confirmed the model’s accuracy, producing a comprehensive database of isotopic compositions across 301 assembly locations and 580 isotopes through the reactor’s initial operation and equilibrium cycle. The model …
War, Wounds, And Strategy: Patient Movement Lessons From The World Wars For Great Power Competition, Phillip R. Jenkins
War, Wounds, And Strategy: Patient Movement Lessons From The World Wars For Great Power Competition, Phillip R. Jenkins
Faculty Publications
This thesis examines the evolution of the U.S. military's patient movement system during World War I and World War II to evaluate how well it may perform under the conditions of future large-scale combat operations. It asks whether the United States can move, treat, and sustain wounded personnel at the scale and pace required to preserve combat power in a prolonged, high-intensity conflict. Using detailed case studies of the Meuse-Argonne Offensive and the Battle of the Bulge, the analysis focuses on how transportation platforms, organizational structure, and standard operating procedures (SOPs) shaped patient movement under conditions of attrition, disruption, and …
Equiangularity From Compatible Orthobiangularity, Tyler J. Myers
Equiangularity From Compatible Orthobiangularity, Tyler J. Myers
Theses and Dissertations
An equiangular tight frame (ETF) is an equal norm sequence of vectors in a Hilbert space whose coherence achieves equality in the Welch bound. Such sequences necessarily have minimal coherence and thus are, in some sense, as "spread out" in space as possible. ETFs have a variety of applications, such as compressed sensing and waveform design. The main problem in the study of ETFs is determining the pairs (D, N) for which an ETF with N vectors in a D-dimensional space exists. Real ETFs are moreover equivalent to a special subset of a well-studied class of graphs known as strongly …
Constraining Nuclear Data Uncertainty Requirements For The 19F(A, N)22Na Reaction For Non-Proliferation Applications, Tyler R. M. Smith
Constraining Nuclear Data Uncertainty Requirements For The 19F(A, N)22Na Reaction For Non-Proliferation Applications, Tyler R. M. Smith
Theses and Dissertations
This thesis explores the requirements on nuclear data uncertainties needed for the use of the 19F(α, n)22Na reaction for nuclear non-proliferation applications. An overview of how neutrons are produced from alpha decays in a UF6 medium is discussed. Calculation demonstrate the role nuclear data uncertainties effect the neutron yield and energy spectra as a function of enrichment.
Contract Quality Feature Extraction Using Llm, Aaron C. Washington
Contract Quality Feature Extraction Using Llm, Aaron C. Washington
Theses and Dissertations
This study explored the potential insights generated from linguistic complexity measurements and large language model (LLM) based assessments on the quality of contract documents. By combining structured True/False prompts with log-probability analysis and ambiguity scoring, the study introduced novel contract-quality assessment methods. Results support a feature-driven approach to contract evaluation, one that offers automated, scalable insights for triaging risk and improving drafting practices. These assessment methods contribute to the growing field of legal natural language processing by offering modular tools for effective contract analysis.
Rotating Scatter Mask System Optimization Study For Determining Optimal Image Recreation, Seth L. Grover
Rotating Scatter Mask System Optimization Study For Determining Optimal Image Recreation, Seth L. Grover
Theses and Dissertations
The Rotating Scatter Mask (RSM) system is a radiation imaging technology currently limited by the mask design and governing identification algorithm parameters. To optimize the RSM design, Dakota—an optimization software—was integrated with a ray tracing code that simulates particle interactions with the RSM detector, and with the Locally Competitive Algorithm (LCA), which reconstructs the source image based on the ray tracing code’s Detector Response Matrix (DRM). Since the original ray tracing code was developed in MATLAB, it was translated into Python to improve compatibility with both Dakota and LCA. The Python version of the ray tracing code was then integrated …
Fused-Silica Microelectromechanical Systems For Relative Gravimetry, Ethan Doerstling
Fused-Silica Microelectromechanical Systems For Relative Gravimetry, Ethan Doerstling
Theses and Dissertations
Gravimeters are devices that measure gravitational acceleration which can be used by the United States Air Force (USAF) in the areas of navigation and remote sensing. Fused-silica microelectromechanical systems (MEMS) devices offer capabilities to make inexpensive relative gravimeters with higher thermal stability than common silicon devices while maintaining good gravitational sensitivity. The fused-silica devices in this research were designed, simulated, fabricated, and tested to observe their performance as gravimeters. The devices exhibit properties of highly sensitive accelerometers but the current designs do not qualify as gravimeters. This study provides information to improve the sensitivity and stability of these fused-silica MEMS …
Leveraging Large Language Models (Llms) For Automated Generation Of Sysml V2 State Machines In Guidance, Navigation, And Control (Gnc) Systems, Andrea Louise Ames
Leveraging Large Language Models (Llms) For Automated Generation Of Sysml V2 State Machines In Guidance, Navigation, And Control (Gnc) Systems, Andrea Louise Ames
Theses and Dissertations
Modern defense systems continue to grow in complexity, placing increasing pressure on engineering workflows to be faster and more adaptable. While Model-Based Systems Engineering (MBSE) with the emerging SysML v2 standard provides a framework for capturing system behavior, its practical use is often limited by the expertise and time required for manual modeling. This research investigates whether large language models (LLMs) can help overcome that barrier by automatically generating SysML v2 state machines from Guidance, Navigation, and Control (GNC) textual inputs. Three LLM Flowise-based models were developed and evaluated: the Structured Transformation Model (STM), which uses a structured extraction and …
Surface Resistivity Correlation To Nano-Defects In Laser Powder Bed Fused Molybdenum (Mo)-Silicon Carbide (Sic) Alloys, Andrew Mason, Larry W. Burggraf, Ryan A. Kemnitz, Nate Ellsworth
Surface Resistivity Correlation To Nano-Defects In Laser Powder Bed Fused Molybdenum (Mo)-Silicon Carbide (Sic) Alloys, Andrew Mason, Larry W. Burggraf, Ryan A. Kemnitz, Nate Ellsworth
Faculty Publications
The integration of Silicon Carbide (SiC) nanoparticles into Laser Powder Bed Fusion (LB-PBF) Molybdenum (Mo) printing represents a significant advancement in refractory metal additive manufacturing. Our investigation examined how varying SiC nanoparticle sizes affect the microstructural and electrical properties of LB-PBF-printed molybdenum components while maintaining a 0.01 mass fraction of Mo. At an Linear Energy Densities (LED) of 1.8 J/mm, the addition of 80 nm SiC particles achieved a 46% reduction in porosity, while sheet resistance decreased by 6% at LED of 2.0 J/mm with 80 nm SiC particles. These performance improvements stem from several mechanisms: SiC particles serve as …
Impact Of Prior Exposures On Biomarkers Of Blast During Military Tactical Training, Zhaoyu Wang, Shengnan Sun, Alis Aksar Kranfli, Jeffrey Nemes, Molly J. Sullan, Andrew J. Hoisington, Lisa A. Brenner, Miciej Skotak, Christina R. Lavalle, Yongchao Ge, Walter Carr, Fatemah Haghighi
Impact Of Prior Exposures On Biomarkers Of Blast During Military Tactical Training, Zhaoyu Wang, Shengnan Sun, Alis Aksar Kranfli, Jeffrey Nemes, Molly J. Sullan, Andrew J. Hoisington, Lisa A. Brenner, Miciej Skotak, Christina R. Lavalle, Yongchao Ge, Walter Carr, Fatemah Haghighi
Faculty Publications
Blast injuries and subclinical effects are of significant concern among those Service Members (SMs) participating in military operations and tactical trainings. Studies of SMs repeatedly exposed during training find concussion-like symptomology with transient decrements in neurocognitive performance, and alterations in blood biomarkers. How prior mild TBI (mTBI) history interacts with low-level blast (LLB) exposure, however, remains unexplored, which we investigate in the present study, to identify interindividual biomarker changes from LLB exposures influenced by prior history of mTBI.
Global Sporadic-E Prediction And Climatology Using Deep Learning, J. A. Ellis, Daniel J. Emmons, M. B. Cohen
Global Sporadic-E Prediction And Climatology Using Deep Learning, J. A. Ellis, Daniel J. Emmons, M. B. Cohen
Faculty Publications
Sporadic-E (Es) is an ionospheric phenomenon defined by strong layers of plasma which may interfere with radio wave propagation. In this work, we develop deep learning models to improve the understanding of Es, including the presence, intensity and height of the layers. We developed three separate models. The first, building off earlier work in (J. A. Ellis et al., 2024, link in AFIT Scholar, 10.1029/2023sw003669), includes only the main features from radio occultation (RO) measurements. The second adds to that time, date, location, geomagnetic and solar indices, solar winds, x-ray flux, weather and lightning. A …
Overview Of Reference Managers, Phillip Elam
Overview Of Reference Managers, Phillip Elam
AFIT Library Knowledge Base
No abstract provided.
Research Skills: Search Strategies, Elizabeth Irvine
Research Skills: Search Strategies, Elizabeth Irvine
AFIT Library Knowledge Base
No abstract provided.
Simultaneous Observations Of Irregular Sporadic E Structures Using The Lwa And A Dps4d, Kenneth S. Obenberger, C. A. Taylor, Jonah J. Colman, Eugene Dao, J. Dowell, J. D. Eccles, Daniel J. Emmons, C. T. Fallen, J. M. Holmes, G. B. Taylor
Simultaneous Observations Of Irregular Sporadic E Structures Using The Lwa And A Dps4d, Kenneth S. Obenberger, C. A. Taylor, Jonah J. Colman, Eugene Dao, J. Dowell, J. D. Eccles, Daniel J. Emmons, C. T. Fallen, J. M. Holmes, G. B. Taylor
Faculty Publications
Multi-instrument studies have recently shed new light on the morphology of sporadic E, especially intense sporadic E. Here we present simultaneous observations of dense sporadic E (Es) structures using the Long Wavelength Array (LWA) radio telescopes and a Digisonde Portable Sounder 4D (DPS4D). Our coordinated observations show that the LWA radio telescopes in central New Mexico can reliably locate regions of dense Es structures as they pass over a Digisonde located over 500 km away in Texas. The LWA appears to be most sensitive to the densest Es structures, which also appear to contain irregularities with vertical …
Global Ionospheric F Region Parameters From Gnss-Pod Limb Measurements: Evaluations And Comparisons With Two Empirical Models - Iri-2020 And Nequick-2, Nimalan Swarnalingam, Dong L. Wu, Dieter Bilitza, Daniel J. Emmons, Cornelius Csar Jude H. Salinas, Artem Smirnov, Yenca Migoya-Oru
Global Ionospheric F Region Parameters From Gnss-Pod Limb Measurements: Evaluations And Comparisons With Two Empirical Models - Iri-2020 And Nequick-2, Nimalan Swarnalingam, Dong L. Wu, Dieter Bilitza, Daniel J. Emmons, Cornelius Csar Jude H. Salinas, Artem Smirnov, Yenca Migoya-Oru
Faculty Publications
An optimal estimation (OE) technique has recently been developed for F region electron density (Ne) using Global Navigation Satellite System (GNSS) limb sounding on low Earth orbit (LEO) satellites (COSMIC-2, Spire, and FengYun-3). This method provides unprecedented spatiotemporal sampling for global monthly Ne climatology within 100–500 km in 2 hr intervals. The global dataset, collected during mid to moderately high solar activity, is compared with leading models: IRI-2020 and NeQuick-2. Diurnal variations in summer, winter, and equinoctial months are examined for the F2-layer peak, as well as the topside and bottomside of the F region. The observed and modeled NmF2 …
A Survey Of Sampling Methods For Hyperspectral Remote Sensing: Addressing Bias Induced By Random Sampling, Kevin T. Decker, Brett J. Borghetti
A Survey Of Sampling Methods For Hyperspectral Remote Sensing: Addressing Bias Induced By Random Sampling, Kevin T. Decker, Brett J. Borghetti
Faculty Publications
Identified as early as 2000, the challenges involved in developing and assessing remote sensing models with small datasets remain, with one key issue persisting: the misuse of random sampling to generate training and testing data. This practice often introduces a high degree of correlation between the sets, leading to an overestimation of model generalizability. Despite the early recognition of this problem, few researchers have investigated its nuances or developed effective sampling techniques to address it. Our survey highlights that mitigation strategies to reduce this bias remain underutilized in practice, distorting the interpretation and comparison of results across the field. In …
Machine Learning For Reactor Power Monitoring With Limited Labeled Data, C. L. Stewart, B. L. Goldblum, R. G. Abbott, L. Appleby, Brett J. Borghetti, V. Hollingshead, J. H. Whetzel
Machine Learning For Reactor Power Monitoring With Limited Labeled Data, C. L. Stewart, B. L. Goldblum, R. G. Abbott, L. Appleby, Brett J. Borghetti, V. Hollingshead, J. H. Whetzel
Faculty Publications
Real-time reactor power monitoring is critical for a variety of nuclear applications, spanning safety, security, operations, and maintenance. While machine learning methods have shown promise in monitoring reactor power levels, there is limited research on their efficacy in label-starved environments. The goal of this work is to assess the feasibility of classifying nuclear reactor power level using multisource data in scenarios with limited labels. Data were collected using low-resolution multisensors at four nuclear reactor facilities: two large research reactors and two TRIGA reactors. Within each pair, one reactor dataset served as the source and the other as the target in …
Analyzing Stability Of Estimates At Completion For Long Duration Development Efforts, Bradley Vuu, Jonathan D. Ritschel, Brandon M. Lucas, Edward D. White
Analyzing Stability Of Estimates At Completion For Long Duration Development Efforts, Bradley Vuu, Jonathan D. Ritschel, Brandon M. Lucas, Edward D. White
Faculty Publications
Defense program managers utilize Earned Value Management (EVM) methodologies to measure, report, and predict the cost and schedule performance of their programs. Previous research conducted by Christensen (1996) and Kim et al. (2019) has shown varied results in the stability of EVM Estimates at Completion (EACs). Stability is defined as a 10% or less deviation from the final EAC at a specified percent completion point of the program. The Christensen (1996) and Kim et al. (2019) studies also noted that program-specific factors, such as phase, can impact the accuracy of EVM metrics. This study builds upon those works by assessing …
The Effects Of Snow Cover On The Dynamic Pressure Of Nuclear Detonation Blast Waves, Adam Card, Andrew W. Decker
The Effects Of Snow Cover On The Dynamic Pressure Of Nuclear Detonation Blast Waves, Adam Card, Andrew W. Decker
Faculty Publications
Blast pressure is the primary military targeting metric for nuclear weapons. Any local conditions that affect blast pressure have the potential for altering nuclear plans, both from defensive and offensive standpoints. Understanding the impact of snow to the blast wave, therefore, provides a benefit both to military planners and to warfighters on the ground, for any operation occurring in arctic environments. No existing data provides a quantitative description of how snow on the ground affects a nuclear detonation blast wave passing over it. Similar blast waves passing over dust have experimentally proven to enhance blast pressure in a localized region.1 …
Distribution Comparisons Of Eac Cost Growth For Aircraft Work Breakdown Structure Elements, Kyle P. Marquis, Edward D. White, Brandon M. Lucas, Robert D. Fass, Jonathan D. Ritschel, Shawn M. Valentine
Distribution Comparisons Of Eac Cost Growth For Aircraft Work Breakdown Structure Elements, Kyle P. Marquis, Edward D. White, Brandon M. Lucas, Robert D. Fass, Jonathan D. Ritschel, Shawn M. Valentine
Faculty Publications
This article analyzes and investigates the distribution of cost growth of the Estimate at Completion (EAC) for the Work Breakdown Structure (WBS) elements of approximately 60 historical United States Acquisition Category I Research, Development, Test and Evaluation aircraft programs. Using the method of maximum likelihood in conjunction with the Akaike Information Criterion, the authors suggest that both the lognormal and Weibull distributions provide relatively good fit to EAC cost growth, with the lognormal slightly edging out the Weibull. As a summarized finding, the authors present their empirical results for the mean, coefficient of variation (CV), the 15th and 85th percentiles …
Afit Librarian Newsletter : Check It Out (Second Quarter 2025), D'Azzo Research Library, Air Force Institute Of Technology
Afit Librarian Newsletter : Check It Out (Second Quarter 2025), D'Azzo Research Library, Air Force Institute Of Technology
AFIT Library Knowledge Base
Greetings. Welcome to the second edition of our quarterly newsletter! We are excited to share with you the latest updates from our library.
Modeling And Characterization Of On-Orbit Servicing Architectures For Efficient Mission Planning, Samantha Q. Vi Tang
Modeling And Characterization Of On-Orbit Servicing Architectures For Efficient Mission Planning, Samantha Q. Vi Tang
Theses and Dissertations
As space-based systems become increasingly critical to global infrastructure, efficient satellite maintenance and resource management have become essential to ensuring operational longevity. On-orbit servicing has emerged as a key strategy for extending satellite lifespans, mitigating space debris accumulation, and enhancing the cost effectiveness of space operations. This study presents a comprehensive mixed-integer programming (MIP) model to optimize servicer task assignments and routing while minimizing propellant consumption. The model captures the operational complexities of servicing a network of satellites across multiple orbits by incorporating realistic constraints, such as fuel limitations and task completion time windows. Sensitivity analysis allows mission planners to …
Key Characteristics Of Long-Lived Portland Cement Concrete Airfield Pavements In The United States Air Force Enterprise, Cameron J. Palmer
Key Characteristics Of Long-Lived Portland Cement Concrete Airfield Pavements In The United States Air Force Enterprise, Cameron J. Palmer
Theses and Dissertations
The United States Air Force maintains a $30 billion portfolio of airfield pavements essential for national security. Despite their importance, budget constraints have limited average annual repair funding to $260 million over the past decade, requiring pavements to last significantly longer than their typical life span. This project addresses the central question: What design features or characteristics contribute to the longevity of Portland Cement Concrete (PCC) airfield pavements? Utilizing data from AFCEC's Airfield Pavement Evaluation (APE) team and the PAVER sustainment management system, this study employed linear regression, logistic regression, and deep artificial neural network (ANN) modeling techniques to identify …
The Impact Of Solar Angle And Cloud Shadows On 3d Reconstruction Of Rolling Stock Cargo, Carlina M. Ostrand, Adam D. Reiman, Frank W. Ciarallo, Scott L. Nykl, Clark N. Taylor, Joshua F. Krutz
The Impact Of Solar Angle And Cloud Shadows On 3d Reconstruction Of Rolling Stock Cargo, Carlina M. Ostrand, Adam D. Reiman, Frank W. Ciarallo, Scott L. Nykl, Clark N. Taylor, Joshua F. Krutz
Faculty Publications
Meeting the relentless demand for more efficient air cargo transportation is of paramount importance for commercial needs and military missions. This study describes an experiment to test an innovative approach that harnesses cutting-edge stereoscopic vision technology to create 3D point clouds of rolling stock cargo across varying solar angles and cloud shadow conditions. Virtual cargo point clouds are generated by calibrating and systematically organizing the depth and location points from an RGB-D camera and then reprojecting them in a virtual environment. Measurement accuracy was rigorously tested across six camera positions in various combinations of weather conditions against physical ground truth …
On Large Language Models In National Security Applications, William N. Caballero, Phillip R. Jenkins
On Large Language Models In National Security Applications, William N. Caballero, Phillip R. Jenkins
Faculty Publications
The overwhelming success of GPT-4 in early 2023 highlighted the transformative potential of large language models (LLMs) across various sectors, including national security. This article explores the implications of LLM integration within national security contexts, analyzing their potential to revolutionize information processing, decision-making, and operational efficiency. Whereas LLMs offer substantial benefits, such as automating tasks and enhancing data analysis, they also pose significant risks, including hallucinations, data privacy concerns, and vulnerability to adversarial attacks. Through their coupling with decision-theoretic principles and Bayesian reasoning, LLMs can significantly improve decision-making processes within national security organizations. Namely, LLMs can facilitate the transition from …
Seal For A Wave Rotor Disk Engine, Christopher Tait, Pejman Abkari, Brian Sell, Marc D. Polanka
Seal For A Wave Rotor Disk Engine, Christopher Tait, Pejman Abkari, Brian Sell, Marc D. Polanka
AFIT Patents
A unique seal for a wave rotor disk engine is disclosed herein. The seal is operable for sealing a region between a rotor and a rotor casing. The seal is spring loaded and will engage with the tip of the rotor to reduce pressure loss within the wave rotor disk engine.
A Multi-Objective Reinforcement Learning Framework For Title Autonomous On-Orbit Inspections, Austin C. Reynolds
A Multi-Objective Reinforcement Learning Framework For Title Autonomous On-Orbit Inspections, Austin C. Reynolds
Theses and Dissertations
The rapidly evolving landscape of space operations necessitates dynamic and autonomous systems to address complex challenges such as Resident Space Object (RSO) inspections. This research explores the application of a Multi-Objective Reinforcement Learning (MORL) framework to rendezvous and proximity operations (RPO), enabling agents to balance conflicting objectives like time efficiency, fuel conservation, and information gain. Unlike traditional reinforcement learning, MORL allows dynamic reweighting of objectives without retraining, offering adaptability and efficiency in multi-objective environments. The study demonstrates MORL's capabilities through custom 2D and 3D simulations of Hill-Clohessy-Wiltshire (HCW) environments and comparing its performance to traditional RL in RPO scenarios. Tasks …