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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 …
Lethality And Survivability Of Autonomous Self-Sensing Uavs, Jeffrey T. Wilson
Lethality And Survivability Of Autonomous Self-Sensing Uavs, Jeffrey T. Wilson
Theses and Dissertations
Unmanned Aerial Vehicles (UAVs) have seen increased usage over the past two decades during the Global War on Terrorism (GWOT), operating in low-risk environments against dispersed enemies with minimal counter-drone capabilities. However, as the U.S. military shifts focus to Multi-Domain Operations (MDO) and Large Scale Combat Operations (LSCO), UAVs face significantly higher risks, including frequent and successful attacks, as well as the exploitation of their technology. Battle damage assessment (BDA) is not new; however, autonomous self-assessment by UAVs represents a novel advancement. Currently, UAV BDA relies on manual inspection, requiring approximately eight hours per drone. By adopting self-sensing technology, UAVs …
Evaluating Dry Air Personnel Decontamination Of Methyl Salicylate As A Chemical Agent Surrogate In Extreme Cold Environments To Reduce Airborne Risks Using A Manikin, Lance E. Campbell
Evaluating Dry Air Personnel Decontamination Of Methyl Salicylate As A Chemical Agent Surrogate In Extreme Cold Environments To Reduce Airborne Risks Using A Manikin, Lance E. Campbell
Theses and Dissertations
This research investigated a mobile air shower as an alternative to water washing for personnel chemical decontamination in Arctic environments, where traditional methods like disrobing and rinsing are impractical. Interest in Arctic operations has grown with recent U.S. military focus on strategic advancements by Russia and China, yet research on air shower effectiveness for chemical decontamination remains limited. This study examined whether a commercial off-the-shelf air shower could effectively decontaminate methyl salicylate (MES), a surrogate for chemical warfare blister agents, from a manikin outfitted in military cold-weather gear. Researchers used a ppbRAE 3000 photo-ionization detector to measure MES concentrations following …
Emergency Response Digital Twin: Integrating Augmented Reality And Live Position Data With Simulation-Aided Decision-Making In Real-Time, Joseph Fuentes
Emergency Response Digital Twin: Integrating Augmented Reality And Live Position Data With Simulation-Aided Decision-Making In Real-Time, Joseph Fuentes
Theses and Dissertations
With the growing use of simulation across industries, the digital twin remains an underexplored research area, particularly in emergency management and response. Its real-time updating capability is often overlooked due to the misconception that "digital twin" is merely a complex term for simulation. This paper highlights its distinctiveness through an evasion exercise involving two independent entities in a collocated environment. Using a highly integrated virtual environment (HIVE) and internet of things (IoT) devices, we link the physical system with an analytical simulation, demonstrating the impact of lag times in high-pressure scenarios. The computational model leverages agent-based modeling (ABM) and discrete-event …
Proximal Policy Optimization Applied To The Beyond Visual Range Air Combat Maneuvering Problem, Daniel B. Joseph
Proximal Policy Optimization Applied To The Beyond Visual Range Air Combat Maneuvering Problem, Daniel B. Joseph
Theses and Dissertations
Artificial intelligence (AI) grows ever-more important in warfighting. Emerging technologies allow for the use of AI to control aircraft and weapons systems. This research investigates the application of reinforcement learning (RL) through the Proximal Policy Optimization (PPO) algorithm to a two-versus-two (2v2) beyond-visual-range (BVR) air combat maneuvering problem (ACMP). Implemented in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM), the methodology frames the engagement as a Markov decision process, wherein an autonomous RL agent learns continuous control decisions—throttle, pitch, roll, and yaw—under a cooperative communication scheme. A multi-phase curriculum-learning approach facilitates the progressive acquisition of flight stability, weapon deployment, …
Investigation Of The Convective Heat Transfer Driving Potential For Hypersonic Flows, Roderick A. Mills
Investigation Of The Convective Heat Transfer Driving Potential For Hypersonic Flows, Roderick A. Mills
Theses and Dissertations
Accurately determining adiabatic wall temperature is critical for characterizing surface heating in hypersonic flows. The differences between adiabatic wall temperature and stagnation temperature for Mach 6 flow are examined. A two-dimensional explicit finite difference scheme was developed to analyze heat transfer within an angled wedge and to assess the applicability of the classical semi-infinite solid solution to the Fourier Heat Equation for estimating adiabatic wall temperature and convective heat transfer coefficients. Experimental surface temperature data were extracted from infrared thermography obtained during Mach 6 wind tunnel tests, and the semi-infinite solid solution was applied to estimate the adiabatic wall temperature. …
Thin-Filament Pyrometry In A Rotating Detonation Engine, Theodore B. Guetig
Thin-Filament Pyrometry In A Rotating Detonation Engine, Theodore B. Guetig
Theses and Dissertations
Rotating Detonation Engine (RDE) proves beneficial by creating a pressure rise across the combustor rather than a pressure drop seen in traditional aircraft combustors. Coupled with this high pressure is a high temperature in the detonation engine that is difficult to measure as it varies spatially and temporally. Thin-Filament Pyrometry (TFP) was performed on a 6-in RDE to measure these high temperatures. Temperature profiles were obtained over a variety of mass flows, equivalence ratios, and different axial locations within the RDE providing insight into the mixing process and where the heat release occurs. Successful determination of these temperature profiles within …
Impact Of Stochastic Travel Times On The Military Port Selection Problem: A Stochastic Programming Approach, William M. Titus
Impact Of Stochastic Travel Times On The Military Port Selection Problem: A Stochastic Programming Approach, William M. Titus
Theses and Dissertations
This research models and analyzes the impact of stochastic travel times on port selection during a large-scale mobilization of equipment from continental United States installations to deployment locations using sealift ships. A stochastic mixed-integer programming model is developed to minimize the average arrival time of equipment into theater. The model is solved using Sample Average Approximation. In the first stage, the model selects ports to open and assigns installations, equipment, and ships to open ports. In the second stage, travel times are realized, and equipment is assigned to specific ships that are scheduled to depart. Results show that the marginal …
Evaluating Educational Benefits Of A Custom Cyber Game: ‘Hvac Attack!’, Jillian S. Valente
Evaluating Educational Benefits Of A Custom Cyber Game: ‘Hvac Attack!’, Jillian S. Valente
Theses and Dissertations
Cyber competition and conflict remain an enduring concern for the Department of Defense (DoD). Positive control of cyberspace is crucial across the vast diversity of military operations and supporting activities. Military members play an important role in cyber prevention, detection, and remediation, but most receive relatively little training outside of the annual Cyber Awareness Challenge. Particular career fields within the DoD may benefit from specialized training in cybersecurity, in particular the civil engineering (CE) community supporting critical infrastructure protection. Prior research has suggested that game-based learning (GBL) can be beneficial for teaching cyber concepts.
Molecular Scale Investigations Of Interactions Between Pfas And Concrete, Eric E. Keeler
Molecular Scale Investigations Of Interactions Between Pfas And Concrete, Eric E. Keeler
Theses and Dissertations
Per- and polyfluoroalkyl substances (PFAS), widely referred to as “forever chemicals,” exhibit high environmental persistence and potential health risks due to their robust carbon-fluorine bonds. These substances are prevalent in aqueous film-forming foams (AFFF), used in industrial and military applications, and are known to contaminate environmental surfaces, including concrete. This study aims to characterize the molecular-level interaction energies of six PFAS species—PFOA, PFOS, PFHxS, PFHxA, 6:2 FTS, and PFBS—with calcium silicate, a key component of concrete, using density functional theory (DFT) calculations. Change in Gibbs free energy (ΔG) was determined for each of the interactions, revealing negative ΔG values for …
Evaluating Weather Effects On Sortie Generation Using Discrete Event Simulation, Markus Case
Evaluating Weather Effects On Sortie Generation Using Discrete Event Simulation, Markus Case
Theses and Dissertations
United States Air Force (USAF) operations rely on sortie generation, a complex system involving aircraft maintenance, operational planning, munitions, security forces, and aircrew. Failures in any of these areas can jeopardize a mission, and extreme weather events such as lightning, high winds, and snow further complicate operations. This thesis examines the impact of extreme weather on sortie generation, focusing on developing a data-driven discrete-event simulation (DES) to predict generation timelines and identify high-risk areas. The model allows users to adjust key inputs, including the month, number of aircraft, processing times, and personnel/equipment availability. By simulating real-world conditions, the model helps …
A Machine Learning/Deep Learning Investigation On Remote Manufacturing Machine State Classification, Ajeet S. Parmar
A Machine Learning/Deep Learning Investigation On Remote Manufacturing Machine State Classification, Ajeet S. Parmar
Theses and Dissertations
Determining the extent of manufacturing capabilities with respect to adversarial or hostile nations is a topic of significant importance to the Department of Defense. Manufacturing capabilities can serve as indications of a nation's industrial power and its economy of force in warfare. Remotely detecting machine operations via electromagnetic sensors may be possible via Deep Learning (DL) and Machine Learning (ML) algorithms. To predict machine states, sensor data is collected externally from a machine shop on a college campus to monitor the operating states of lathes and mills in individual and concurrent operation. Furthermore, several sensors are placed in various positions, …
Uranium Particle Classification Using Statistical Machine Learning And Deep Neural Networks For Nuclear Forensics, Lee C. Lambert
Uranium Particle Classification Using Statistical Machine Learning And Deep Neural Networks For Nuclear Forensics, Lee C. Lambert
Theses and Dissertations
The classification of uranium particles from scanning electron microscopy (SEM) imagery is critical to nuclear forensics, but has traditionally relied solely on skilled analysts whose classification accuracy and procedures may vary widely. Existing morphology lexicology [1] provides standardization guidelines to aid analysts but cannot fully address analyst variability. Using a dataset of 1,906 SEM images across 13 unevenly distributed particle classes and 73 magnification levels, final accuracy between statistical and deep learning methods were compared to find the best classification techniques. Ultimately, the deep learning model achieved an impressive 82% accuracy (80% balanced accuracy) on a withheld test set. This …
Cognitive Warfare In The South China Sea: Analyzing Media Influence And Public Opinion In Taiwan, Wyatt F. Blatti
Cognitive Warfare In The South China Sea: Analyzing Media Influence And Public Opinion In Taiwan, Wyatt F. Blatti
Theses and Dissertations
In a time where conflict extends beyond traditional battlefields, cognitive warfare emerges as a powerful tool to influence perceptions and gain strategic advantages. This study investigates China’s cognitive warfare strategies against Taiwan through trend analysis, topic modeling, and sentiment analysis of news media articles from March 2013 to August 2024 to uncover evolving techniques and mitigation efforts. The findings highlight the potential for tracking cognitive campaigns overtime but will require more than news media alone and suggests future research to better understand indicators of cognitive warfare.
Assessing The Feasibility Of Managed Aquifer Recharge For The United States Air Force, Daniel Hendrix
Assessing The Feasibility Of Managed Aquifer Recharge For The United States Air Force, Daniel Hendrix
Theses and Dissertations
Water stress is becoming an increasing global issue, with 4 billion people (50% of the world’s population) experiencing water stress at least one month per year. By 2050, 60% of the world’s population and $70 trillion USD in global gross domestic product will be affected. This research analyzes 78 CONUS USAF installations to determine location-specific water stress and feasible Managed Aquifer Recharge (MAR) solutions. Although thousands of MAR projects have been implemented globally, active-duty USAF installations have yet to contribute to solving this growing issue. Important factors such as required subsurface conditions, physical limitations, design, cost, and regulatory constraints are …
Cloud One Migration Duration And Its Drivers, Grayson T. Hall
Cloud One Migration Duration And Its Drivers, Grayson T. Hall
Theses and Dissertations
As modern warfare evolves with rapid technological advancements, cloud computing plays a critical role in managing the vast amounts of data required for real-time decision making, as well as enabling seamless organizational access to mission-critical programs and information from around the globe. Recognizing its importance, the Department of Defense (DoD) identified cloud computing as essential for maintaining the military’s technological edge. However, despite cloud computing’s strategic significance, the DoD faces challenges in successfully implementing department-wide cloud computing. In contrast, the Air Force’s cloud computing environment, Cloud One, is fully operational and has already integrated over 145 systems into its platform. …
Isotopic Analysis Of Lithium Hydroxide Monohydrate Using Laser-Induced Breakdown Self-Reversal Isotopic Spectrometry (Libris) And Machine Learning, Madison R. Moran
Isotopic Analysis Of Lithium Hydroxide Monohydrate Using Laser-Induced Breakdown Self-Reversal Isotopic Spectrometry (Libris) And Machine Learning, Madison R. Moran
Theses and Dissertations
High-resolution Laser-Induced Breakdown Self-Reversal Isotopic Spectrometry (LIBRIS) is implemented to record the 15.8 pm Li I 670.8 nm isotopic shift in LiOH · H2O samples of varying 6, 7Li abundance. A simple univariate linear regression demonstrates an acquired isotopic shift of 13.813 ± 1.21 pm in samples varying from 3 to 95 6Li at%. Supervised machine learning regressions are trained on self-reversal wavelength locations in order to quantify 6Li abundance. A stacked ensemble using two base learners yields the superlative characterization of 6Li abundance with RMSE of 5.66 at% and detection limit of 18.8 at%. Using …
Palindrome: A Bi-Directional Multi-Object Detection Framework For Relative Navigation And Autonomous Docking, Liam A. Weinfurtner
Palindrome: A Bi-Directional Multi-Object Detection Framework For Relative Navigation And Autonomous Docking, Liam A. Weinfurtner
Theses and Dissertations
This work introduces a bi-directional, multi-object detection framework that integrates pose estimates from both receiver- and tanker-mounted cameras to improve accuracy and redundancy. A modular YOLO-based detection pipeline is trained using synthetic and real imagery, leveraging a bootstrap transfer learning approach to enhance sim-to-real performance. System evaluation in both virtual and real-world environments demonstrates improved detection robustness, pose estimation accuracy, and scalability. These advancements contribute to the development of AI-driven vision systems for AAR and other autonomous docking applications.
Design And Evaluation Of A Deployment Mechanism For A Space-Based Origami Mirror In Cubesat Missions, Arturo Luna
Design And Evaluation Of A Deployment Mechanism For A Space-Based Origami Mirror In Cubesat Missions, Arturo Luna
Theses and Dissertations
The space environment’s suboptimal lighting conditions and eclipse periods impede inspection and on-orbit servicing missions. To abate this problem, the Air Force Institute of Technology (AFIT) launched the Mirror Illumination for Reconnaissance and Rendezvous of Orbital Resident Systems (MIRRORS) initiative. This initiative provides augmented illumination to resident space objects (RSOs) by reflecting the sun’s rays from a mirror satellite. Leveraging origami-inspired designs for compact packaging, AFIT developed a cube origami flasher mirror constructed from aluminum panels that unfolds passively via Nitinol hinges. This research investigates the necessary deployment and panning mechanisms for a dynamic, space-based origami mirror to advance the …
Weighed And Measured: Toward An Evaluation Method For Digital Models In Source Selection, Liam N. O'Neill
Weighed And Measured: Toward An Evaluation Method For Digital Models In Source Selection, Liam N. O'Neill
Theses and Dissertations
Inspired by a recent model-based source selection conducted by the Advanced Range Threat System (ARTS) Program Office at Hill AFB, this research effort explored the development of new tools the DoD could use when evaluating models submitted with proposals. Specifically, the effort aimed to incorporate the Multi-Objective Decisions Analysis (MODA) framework into SysML diagrams as a solution for technical evaluations on models submitted with offeror proposals, eventually producing the Model-Based Decision Tool (MBDT). The MBDT is built from a Value Hierarchy based on key system requirements, each weighted by priority and measured by their own Single-Dimensional Value Functions (SDVFs). By …
Best Estimate Reconstruction Of The Control Profile For A Maneuvering Reentry Vehicle, Justin R. Evans
Best Estimate Reconstruction Of The Control Profile For A Maneuvering Reentry Vehicle, Justin R. Evans
Theses and Dissertations
Astrodynamic reentry is an increasingly important flight regime as countries around the world develop new spacecraft and weapons. A design of interest is that of hypersonic glide vehicles with the ability to maneuver in the atmosphere. As these vehicles become more common across the world, it has become advantageous to track foreign reentry tests in order to determine capabilities. While current observation techniques may allow position and velocity to be tracked across the trajectory, orientation of the vehicle is not always observable. The ability to find vehicle orientation across time gives insight into the performance characteristics of the vehicle. This …
Investigation Of Neutron Inelastic Scatter Cross Sections On 16O, Molly A. Wakeling
Investigation Of Neutron Inelastic Scatter Cross Sections On 16O, Molly A. Wakeling
Theses and Dissertations
Nuclear data underpin a number of applications across nuclear reactor design, medicine, astrophysics, nonproliferation, national security, and other related fields. However, data are conflicting or missing across the spectrum of isotopes and reactions, and theoretical calculations can only go so far to predict nuclear properties. A key reaction with incomplete data is neutron inelastic scatter on 16O, which reduces neutron energies and produces high-energy gamma rays that are often not taken into account in simulations of nuclear reactors, nuclear weapon detonations, nuclear fusion, and other areas. To fill these gaps, the Gamma-Energy Neutron-Energy Spectrometer for Inelastic Scattering, or GENESIS, …
Early-Phase Cost Estimation For Department Of Defense Construction Projects Via Artificial Neural Networks: Implications And Policy Recommendations For Artificial Intelligence Integration, Kobe T. Thompson
Theses and Dissertations
The Department of Defense lost over 500 million dollars between 2016 and 2024, partially due to poor early cost estimates resulting in cost overruns. practice for cost estimation relied on parametric techniques that incorporate historical data, subject matter experts in cost estimating, and predictive software applications. The main motivation for this study was to assess the viability of artificial neural networks as a means of providing a more accurate cost estimate in the early design phases of a construction project. The dataset initially contained approximately 48,000 data points from a database of various Air Force projects, including maintenance, repair, minor …
Military Entrance Processing Station Location And Capacity Optimization, Micah A. Hurst
Military Entrance Processing Station Location And Capacity Optimization, Micah A. Hurst
Theses and Dissertations
This research optimizes the number, placement, and capacity of Military Entrance Processing Stations (MEPS) to minimize applicant and recruiter travel and improve recruitment efficiency. Using mixed-integer programming, it develops capacitated facility location (CFLP) and maximal covering location (MCLP) models, considering facility capacity, budget, and geographic coverage. Computational testing and scenario evaluations highlight opportunities to reduce travel and balance capacity. For example, the CFLP model adds three new MEPS, reducing annual applicant travel by 1.2 million miles in Florida and Texas and 1.0 million in California, while increasing accessibility within 60 miles of a MEPS. This data-driven approach provides USMEPCOM with …
Machine Learning Techniques To Detect Anomalies In T-38 Flight Sensor Data, Sydney M. Wekamp
Machine Learning Techniques To Detect Anomalies In T-38 Flight Sensor Data, Sydney M. Wekamp
Theses and Dissertations
Accurate sensors are critical for ensuring the safety of aircrew. However, detecting faulty sensors remains a significant challenge for the Test Pilot School at Edwards Air Force Base in California. Current methods rely on either student pilots identifying anomalies or waiting for sensors to fail completely before repairs are made—an approach that lacks reliability and consistency. This research aims to address these shortcomings by implementing machine learning techniques to detect sensor faults proactively. To date, applying machine learning to a dataset of this size, encompassing numerous sensors on the same aircraft, is unprecedented. The project focuses on establishing strong baseline …
Forecasting Army Recruiting Mission Distribution, Maxwell A. Deihl
Forecasting Army Recruiting Mission Distribution, Maxwell A. Deihl
Theses and Dissertations
The Army’s recruiting landscape has changed markedly in recent years, raising questions about whether forecasting methods of Army contracts remain robust. This thesis recreates the presented models in Joshua McDonald’s 2015 thesis. It replicates and evaluates the models with updated data (2018–2023) to assess their current validity and compare them to novel alternative approaches, such as simpler regression models or neural networks. While the 2015 model remains a valuable baseline, results suggest that either refining its variables or adopting alternative methods can improve predictive accuracy and interpretability. Ultimately, the United States Army Recruiting Command has many options regarding how it …
Advancing Defense Software Cost Estimation Through Regression, Probabilistic, And Machine Learning Models, Stephen D. Chatterton
Advancing Defense Software Cost Estimation Through Regression, Probabilistic, And Machine Learning Models, Stephen D. Chatterton
Theses and Dissertations
Accurately estimating software costs is critical for effective project management within the Department of Defense (DoD), where early decisions shape resource allocation and risk management. This work evaluates regression-based Cost Estimating Relationships (CERs), probabilistic models, and machine learning techniques to address limitations of traditional estimation methods. Using records from two DoD repositories, the analysis applied Ordinary Least Squares (OLS) regression, Multinomial Logistic Regression (MLR), Random Forest, and neural networks to model and classify software costs, with key predictors including Source Lines of Code (SLOC), Equivalent Source Lines of Code (ESLOC), and programming hours. The findings highlight strengths and trade-offs of …
The Location Set Covering Disruption Problem, Richard A. Sheldon
The Location Set Covering Disruption Problem, Richard A. Sheldon
Theses and Dissertations
This research models and analyzes a variant of the Location Set Covering Problem (LSCP) in a bilevel, game theoretic setting by posing the LSCP as a non-cooperative attacker-defender Stackelberg game, where facilities are to be emplaced by the defender from a boarder set of potential facility locations to cover a set of demands; however, an attacker removes the possibility of emplacing q specific facility locations with the objective to remove the maximum weighted value demands, and then lexicographically maximize the cost of coverage of remaining demands. A novel methodology leveraging lexicographic programming computed an optimal solution for 98% of all …
Practical Estimation Of Action-Generation Mechanisms In Repeated Games, Vladimer Kellachow Iii
Practical Estimation Of Action-Generation Mechanisms In Repeated Games, Vladimer Kellachow Iii
Theses and Dissertations
The goal of this research is to gain insight into how players of a game learn their strategy during the course of repeated play. The study employs the Experience Weighted Attraction (EWA) model, developed by Dr. Colin F. Camerer and Dr. Teck-Hua Ho, as the foundational behavioral framework. Using historic observed strategy decisions, the parameter values that define an opponent’s learning process are updated using various inference methods.
Evaluating A Military Digital Badging System Prototype, Benjamin T. Pederson
Evaluating A Military Digital Badging System Prototype, Benjamin T. Pederson
Theses and Dissertations
The Department of Defense is committed to developing and maintaining a highly skilled workforce capable of defending the United States and associated interests abroad. Digital badging systems, a form of micro-credentialing, offer a way to record service member competencies. By providing decision-makers with granular data, this technology could augment the military’s development of a highly skilled workforce, especially in technical career fields including cyber operations. Mixed-method data from thirty-six participants suggest that establishing a digital badging program could increase deterrence and operational effectiveness.