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Design and Deployment of a Hybrid Neural Network for Real-Time GPS Correction in Slow Moving, Low SWaP, UAV Flight Systems
(2025-06-29) Nathan, Gokul; Cucioglu, Alperen; Makhsous, Sep
The global surge in demand for unmanned aerial vehicles (UAVs), projected to reach $91 billion by 2033, spans diverse applications in urban environments. However, achieving accurate horizontal geo-localization in urban settings remains challenging due to poor GPS performance, creating a significant barrier to effective drone opera-tions, even in static or slow-moving deployments. Recreational-grade GPS systems, commonly used in low Size, Weight and Power (SWaP) UAVs, suffer from residual errors exceeding 100 meters in urban environments, severe-ly limiting real-time navigation accuracy and drone autonomy. Although capable of good accuracy in conven-tional aerospace deployments, existing traditional aerospace-ready GPS receivers are expensive, power-intensive, and heavy, making them unsuitable for low SWaP drone deployment. Existing performance-enhancing post-processing techniques for recreational GPS are limited by the real-time requirements and the noise, power, and computational constraints inherent to drone operations. This underscores the critical need for accurate, ro-bust, lightweight, and power-efficient urban localization solutions for UAVs. In this work, we present a hybrid Integrated Recurrent Neural Network–Deep Neural Network (IRNN-DNN) architecture, capable of running on em-bedded microcontrollers with recreational-grade L1C GPS receivers. Leveraging Long Short-Term Memory (LSTM) networks, the IRNN-DNN model captures temporal dependencies and spatial error patterns across multi-ple concurrent GPS receivers, allowing real-time error correction. Field tests with MTK3339 GPS receiver on UAVs in static test conditions showed, with a 2D residual error of 1.71 meters at 10 Hz, a 30% improvement over existing models—validate the IRNN-DNN approach as a promising embedded solution for enhancing urban lo-calization performance in constrained UAV applications.
A numerical strategy for investigating internal circulation in droplets
(2021-12-29) Lin, Yushu; Setiya, Meha; Palmore, John, Jr.
The current study uses numerical methods to investigate the effect of internal circulation on droplet dynamics. In jet atomization simulations, tracking all droplets is computationally expensive. A common way to reduce the computational expense is to use a Lagrangian approach to represent small droplets. To better model these droplets in high-temperature and high-pressure environments, the underlying physics of their behavior needs to be better understood. In such environments, droplet shape and internal circulation should be considered. To study how droplet shape and internal circulation influence droplet dynamics, we will use direct numerical simulation (DNS) to simulate a droplet falling at its terminal velocity in high-temperature, high-pressure air. An in-house code developed for interface-capturing DNS of multiphase flows will be used for the computation.
Interface-capturing numerical studies of multicomponent spray and droplet vaporization
(2021-12-29) Palmore, John, Jr; Lin, Yushu
Motivated by the goal of improving the theoretical understanding of spray combustion, this work studies the vaporization of multicomponent sprays and droplets. It presents an exploratory study of spray vaporization but focuses on the physics of droplet vaporization. For real fuels, which consist of multiple chemical species, or components, the combustion properties of the fuel change over time depending on which components are most prevalent in the vapor. This variation in vapor composition is driven primarily by differences in the vaporization rates of the components. Although much is known about the vaporization of fuel droplets, many studies have relied on simplifying assumptions. Recent work has begun to demonstrate that some neglected phenomena, such as droplet deformation and internal liquid flow, have appreciable effects on fuel-droplet vaporization. This work investigates how droplet deformation and internal flow affect the vaporization of a Jet-A surrogate. It uses an in-house finite-volume code to perform interface-capturing direct numerical simulations of liquid–gas flows. The code simulates the dynamics of multicomponent fuel vaporization from first principles by directly solving the governing conservation laws for mass, momentum, and energy transport. The results quantify how these phenomena affect vaporization in terms of appropriate nondimensional groups, such as the Weber number and circulation strength, and discuss the resulting trends.
PREDICTING MOTION OF ENGINE-INGESTED PARTICLES USING DEEP NEURAL NETWORKS
(2023-02-08) Bowman, Travis; Miranda, Cairen J.; Palmore, John, Jr
The ultimate goal of this work is to facilitate the design of gas turbine engine particle separators by reducing the computational expense to accurately simulate the fluid flow and particle motion inside the separator. It has been well-documented that particle ingestion yields many detrimental impacts for gas turbine engines. This ingestion is of concern for operation in environments where dust, ash, or ice persist. The consequences of ice particle ingestion can range from surface-wear abrasion to engine power loss. Ice particles are chosen for this study because of their relevance to civil aviation. It is known that sufficiently small particles, characterized by small particle response times (τp), closely follow the fluid trajectory whereas large particles deviate from the streamlines. The behavior of small particles hints at a method for larger particle trajectories because the higher order terms (HOT) in the asymptotic particle acceleration solution can be shown to be O(τp). By explicitly considering τp, these HOT can be derived. Rather than manually deriving these terms, this work chooses to implicitly derive them using machine learning (ML). Inertial particle separators are devices designed to remove particles from the engine intake flow. Particle separators contribute to both elongating the lifespan and promoting safer operation of aviation gas turbine engines. Complex flows, such as flow through a particle separator, naturally have rotation and strain present throughout the flow field. This study attempts to understand if the motion of particles within rotational and strained canonical flows can be accurately predicted using supervised ML. This report suggests that preprocessing the ML training data to the fluid streamline coordinates can improve model training. Furthermore, this work provides some guidelines for applying ML, particularly deep feed-forward neural networks, with physics driven multiphase flow data. Additionally, the ML model is able to predict the particle accelerations in the fully rotational and irrotational canonical laminar flows quite well. For combined flows with rotation and strain, however, the model struggles to predict the particle accelerations.
An Assessment of the Local Government's Capacity to Effectively Manage Alaska's Harbors
(2025-06-09) McGavin, Stephanie Hernandez
Port and harbor infrastructure is essential to the State of Alaska’s (State) transportation system and economy. The State constructed the majority of this infrastructure and maintained it on behalf of local communities. Following declining oil revenues in the mid-1980s, the State began the process of transferring harbor facilities to local governments, with many transfers occurring throughout the 1990s and early 2000s. Many of the facilities were at or nearing the end of their useful service life at the time of transfer. This research adopted a Soft Systems Methodology (SSM) approach to determine how the capacity of local governments evolved to sustainably provision operations, maintenance, and improvements of harbor facilities since the transfer of ownership from the State. Additionally, the research defined the barriers local governments face in developing capacity. Results constructed historical information related to the harbor transfer, including harbor conditions, transfer timelines, purchase price, and deferred maintenance funds. Findings suggest that the infrastructure received in poor condition has improved marginally since the transfer and still requires considerable improvements. Key findings indicate that local governments have grown their financial, technical, and institutional capacity to more sustainably manage harbor infrastructure since the transfer, but that the governments face obstacles in becoming fully self-sufficient. Developing sustainable user fees and financing major improvement and replacement projects are major barriers. Local governments additionally face challenges with harbor staffing and technical knowledge-building through both personnel experience and documented operation and maintenance (O&M) plans. Lastly, results indicate that there is a discrepancy in the local and State government perspectives about how to overcome barriers but also mutual understanding that they face similar challenges in managing public infrastructure. The strong, positive relationships between harbor departments and their local governments and users will aid in conversations about closing the gap between what users can afford and what harbors must charge for sustainable operations. Should subsidization of operations be necessary, these relationships are still valuable in developing additional local revenue streams for support of harbor facilities. Strong and positive regional relationships between harbors also provides opportunities to improve regional economic cooperation and development to support local governments. Additionally, harbors are commonly staffed by those with a vested interest in supporting those they serve, whether due to a personal or family background in careers such as commercial fishing or the U.S. Coast Guard. This aids employee understanding of harbor operations and promotes meaningful employment. This research finds that development and execution of O&M plans should be prioritized to better support these staff and assist in valuable knowledge transfer, especially to counteract challenges in staff turnover, recruitment, and training. Local governments find the Alaska Municipal Harbor Facility Grant Program to be incredibly valuable in supporting large and expensive harbor improvement and replacement projects that communities may not otherwise be able to afford. Suggested improvements to this program include an agreement for a minimum annual amount of State program funding; refocused scoring criteria to reflect the need for continued State support; increasing the funding cap amount to reflect the considerable increase in material costs since the program was introduced; and specific attention to supporting smaller or economically disadvantaged communities. Lastly, this research suggests more candid, collaborative conversations between the local and State governments about mutual expectations for self-sustaining governance and support.
Predicting Particle Acceleration Using Flow Feature Extraction in a Louver Particle Separator
(2023-01-19) Bowman, Travis; Miranda, Cairen; Palmore, John, Jr.
The goal of this work is to facilitate the design of gas turbine engine particle separators by reducing the computational expense necessary to accurately predict particle motion with machine learning (ML). It is well known that article ingestion has many detrimental impacts on gas turbine engine operation. Particle ingestion is especially of concern in environments where dust, ash, or ice persist. Accordingly, devices called particle separators are used to remove the particles before they enter the engine. The goal of this work is to improve prediction of particle motion in separators. Ice particles are chosen for this study because of their relevance to civil aviation. The louver particle separator developed by [1] with computational studies is used as a baseline for this work. It has been shown that particle acceleration can be reasonably well-predicted in simple laminar flows using deep feed-forward neural networks [2]. However, there exists a gap for predicting particle acceleration in engineering flows such as the louver geometry of [1]. This is the topic of the current paper. The work builds upon work for laminar flows [2]. In the current work, the complex flow around the louver is decomposed into a laminar shear flow and laminar vortex, and results from [2] are used to analyze the decomposed flows. The accuracy of this decomposition for capturing particle trajectory in the louver is evaluated.
Coding Manual for “Wilder Interaction with Nature Predicts Deeper Presence: Toward a Human-Centered Sustainability Indicator Beyond Exposure”
(2026-10-18) Batra, Arshia; Romera, Mayes Robertson; Schoenfeld, Drew; Sparks, Gillian; Kahn, Peter H. Jr.
The coding manual in this report codes two separate dimensions of people’s accounts of an experience in nature: the depth of Presence they experienced, and the wildness of their interaction with nature. We developed it in a study of 44 adults interviewed about an experience of Presence in nature. Presence is a state of being in which conditioned thinking subsides and the mind becomes open, aware, non-reactive, and still (Kahn et al., 2026). The first part, Presence Levels Coding, describes ten ordered Levels of Presence, from thought-immersed distraction (Level 1) to states described in contemplative traditions (Levels 9 and 10), with definitions, prototypical examples, vignettes, explanatory factors, and a coder cheat-sheet. The second part, Wildness Levels Coding, specifies two measures grounded in the Interaction Pattern approach: the wildness of interaction an environment affords (Maximum IP Wildness) and the wildness of interaction a participant actually enacted (Enacted IP Wildness), together with contextual factors, decision rules, and exemplar cases. A final section explains how to construct the synthesis blocks, the narrative summaries of interview transcripts on which coding was based. Our goal is to make these methods available so that others can use, test, adapt, and build on them.
Light Upon Obfuscation: Russia's Kinetic Grey War Against NATO and Its Implications for Global Hostilities
(2026) Desira, Christian; Radnitz, Scott
Since the 2022 Russian Invasion of Ukraine, the Russian Federation has embarked on a heightened campaign of kinetic grey zone warfare against NATO member states. This campaign began most ostensibly in response to Western support for Ukraine, but also to advance Russian security interests. Incidents of sabotage, cyber warfare, drone incursions, and border violations have accelerated dramatically in NATO territory. Incidents like these are the substance of Grey Zone warfare, defined as the employment of coercive actions by one power against another through covert, deniable, or ambiguous means and outcomes. Random events of a disruptive or destructive nature appear to continue to occur, but with little official recognition of the totality of the looming threat. Kinetic grey zone operations specifically are those that cause physical harm, destruction, or coercion. These incidents have co-occurred with blatantly hostile rhetoric and military provocations by Russia against NATO allies. While the intentions and motivations behind these operations are, by nature, difficult to surmise, analysts have identified key themes behind these kinetic grey-zone provocations and attacks. With truth often found in old historical patterns and modern security interests, the evidence points to an effort to coerce and destabilize NATO. The deterrence of providing further aid to Ukraine, the redirection of policy, and the sowing of fear and confusion within alliance member states all appear to be likely objectives. This analysis begins by defining grey zone warfare, what it looks like, and how powers may employ it as a subtler means of coercion. It then examines Russian history and the motivations behind the modern Russian campaign of imperial aggression. It then assesses the tools Russia uses, how they work together to facilitate operational success, and how defenders can confront and counter them. Grey zone operations are relevant beyond the European security context, and this analysis ends by touching on how Russian activities have contributed to a more dangerous global security environment. All information and analysis were current as of June 1st, 2026.
String-stable constant-spacing platoons without communication
(2026-10-15) Chang, Henry; Lin, Yudong; Devasia, Santosh
This work considers string instability in constant-spacing-policy (CSP) platoons without communication. It is well known that platoons will have string instability without communication with CSP if the vehicle dynamics has at least two poles at the origin. This work considers a modification of the vehicle dynamics, using input-output feedback linearization along with stable-pole zero cancellations, to retain only one pole at the origin. As a result, it is possible to design CSP that is string stable without communication. A disadvantage, with the reduction to only one pole at the origin, is that there is non-zero steady-state error for a constant platoon-speed. However, it is shown that a Taylor-approximation-based, delayed-self-reinforcement (Taylor-approximated DSR) approach can substantially reduce the overall (transient and steady-state) spacing errors while avoiding string instability. Comparative simulations using a benchmark vehicle model show that the proposed Taylor-approximated DSR can reduce the overall error substantially (by 95%) when compared to the standard predecessor-following CSP platoons. Moreover, the resulting steady-state spacing deviation is more than 2 orders of magnitude less when compared to other string-stable approaches such as the constant-time-headway (CTH) policy.
The Effect of Temperature on the Regenerative Abilities of Pleurobrachia bachei
(2026-07) Thompson, Carter
The sea gooseberry (Pleurobranchia bachei) is a common sight at the surface of the waters around Friday Harbor, WA. As comb jellies of the phylum Ctenophora, their small bodies are fragile and vulnerable to exterior damage (van Paridon, 2024). Fortunately, they have also evolved extremely fast regenerative capabilities, with some species even able to fully regenerate up to 75% of their body mass from just a small portion in under 48 hours (van Paridon, 2024). The extent of P. bachei’s ability to regenerate is not as well documented, so I wanted to examine how these regenerative abilities are tested against increased stress from environments more extreme than what they’re used to. Over the course of several days, I looked at how well P. bachei regenerates its tissue while under a drastic increase in ambient water temperature.