Scholarly Works, Aerospace and Ocean Engineering

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Research articles, presentations, and other scholarship

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Space Network (SpaceNet) Testbed - Development of a Multi-Functional Testbed for Simulating Space Communication Networks
Downs, John S.; Barbour, Bruce; Kedrowitsch, Alexander; Mhadgut, Deven; Aryan, Suryansh; Kenyon, Samantha P. (American Institute of Aeronautics and Astronautics, 2025-01-03)
Low Earth Orbit mega-constellations make up a large portion of modern developments in space communications. The ability to simulate and properly research these complex systems is currently being developed to answer questions on the capabilities of the rapidly expanding industry. The development and improvement of a simulation-based platform for satellite network testing can help research efforts to enable industry and government entities to work together in the growing enterprise. The ongoing development of the SpaceNet Testbed is investigated, detailing its use of orbit-based constellation dynamics modeling, software-based emulation, and a hardware-in-the-loop integration design. An overview of the testbed’s software infrastructure design is described alongside details regarding the make-up of its hardware components. Use cases are presented comparing the differences in performance between the satellite network of an actual Starlink mega-constellation currently in orbit and a custom constellation with the same quantity of satellites, but with ideal node spacing and initial orbital positioning. Results of these use cases are then discussed, focusing on the latency of the data traffic and how it differs when varying the testbed’s user-defined configurations. In the future, resiliency testing and ground station to satellite link behavior analysis can be included into the testbed. The testbed has potential to help lead efforts in simulating complex space communication systems.
Using Predicted Earth Angular Momentum for Earth Orientation
Stamatakos, Nicholas; McCarthy, Dennis; Salstein, David; Psiaki, Mark L. (2024-01-22)
Previous investigations have shown the potential of enhancing the accuracy of estimates of the direction of the rotational pole and velocity ofrotation of the Earth by applying atmospheric and ocean angular momentum data (based on global conservation of the angular momentum in theEarth system). This poster is a continuation of previous efforts with additional and/or enhanced modeling of the atmospheric and oceanphenomena and, additionally, enhanced optimal smoothing techniques for combining these and other data to make improved and robust estimates.The atmospheric and ocean data are to be provided by the US Navy Earth System Prediction Capability (ESPC) models, and this data will becombined with past geodetic observations from other inputs using a Kalman Smoother/Filter or other optimal estimation technique.
GNSS Software-Defined Radio: History, Current Developments, and Standardization Efforts
Pany, Thomas; Akos, Dennis; Arribas, Javier; Bhuiyan, M. Zahidul H.; Closas, Pau; Dovis, Fabio; Fernandez-Hernandez, Ignacio; Fernandez-Prades, Carles; Gunawardena, Sanjeev; Humphreys, Todd; Kassas, Zaher M.; Salcedo, Jose A. Lopez; Nicola, Mario; Psiaki, Mark L.; Ruegamer, Alexander; Song, Young-Jin; Won, Jong-Hoon (Institute of Navigation, 2024-01-24)
Taking the work conducted by the global navigation satellite system (GNSS) software-defined radio (SDR) working group during the last decade as a seed, this contribution summarizes, for the first time, the history of GNSS SDR devel-opment. This report highlights selected SDR implementations and achievements that are available to the public or that influenced the general development of SDR. Aspects related to the standardization process of intermediate-frequency sample data and metadata are discussed, and an update of the Institute of Navigation SDR Standard is proposed. This work focuses on GNSS SDR implementations in general-purpose processors and leaves aside developments conducted on field programmable gate array and application-specific integrated circuit platforms. Data collection systems (i.e., front-ends) have always been of paramount importance for GNSS SDRs and are thus partly covered in this work. This report represents the knowledge of the authors but is not meant as a complete description of SDR history.
Classification of Authentic and Spoofed GNSS Signals Using a Calibrated Antenna Array
Esswein, Michael C.; Psiaki, Mark L. (Institute of Navigation, 2025-01-03)
New optimization-based methods have been developed to use measured direction-of-arrival (DoA) information in order to classify received global navigation satellite system signals into authenticated and spoofed sets and to augment that information with pseudorange information when DoA information alone is insufficient to achieve the needed classification. These methods are designed for a system that is being developed to mitigate spoofing and jamming by using signals from a controlled radiation pattern antenna. These new spoofing classification methods operate on DoA outputs from trackers of various signals. This paper presents a multi-hypothesis test that considers all possible hypotheses regarding the authenticated and spoofed sets of tracked signals. A combinatorial analysis is performed in which all possible authenticated-set/ spoofed-set classifications are generated for a given set of tracked signals and the correct authenticated set is determined among the different combinations. Results from Monte Carlo simulations show that using a combined DoA and pseudorange method is suitable for determining the correct combinations.
Advanced Charge Control Dynamics Simulation for the LISA Gravitational Reference Sensor
Kenyon, Samantha Parry; Apple, Stephen; Siu, John; Wass, Peter J.; Conklin, John W. (IOP Publishing, 2025-01-27)
A gravitational wave detector in space, the Laser Interferometer Space Antenna (LISA) will be able to detect gravitational waves in the frequency range of 0.1 mHz–1 Hz, adding to humanity's knowledge of the dark cosmos. The LISA gravitational reference sensor contains a test mass (TM) and is used to determine the local inertial reference frame and as endpoints for the interferometry. The TM is surrounded by an electrode housing to detect changes in TM position and orientation, which is fed back to the spacecraft thrusters for drag-free control. As seen on LISA Pathfinder, the TM builds up charge over time from the space environment and needs to be discharged in order to keep the resulting force noise as low as possible. The operation of intelligently discharging the TM is known as charge control, and is one area of improvement to be explored for LISA. To explore new methods of TM discharge, UV LEDs will be pulsed synchronized with an existing 100 kHz high frequency electric field to facilitate photoelectron current direction and to achieve lower UV light powers by duty cycling. This paper addresses new pulsed methods for the LISA Charge Management System, which require in-depth modeling, analysis, and testing because space environment validation will not be possible prior to LISA launch. Therefore, it is necessary to model the dynamics of charge movement to determine the force noise contribution of pulsed continuous charge control. The charge dynamics model is described, and simulation results featured for charge control efficacy in a deep space radiation environment. Experimental testing of the simulation results could be done in the University of Florida Torsion Pendulum, a key technology to testing GRS performance in a space-like environment.
Understanding the Effect of Build Direction and Scanning Strategy on the Tensile Response of Additively Manufactured in 625 with Innovative Calibration Strategy
Saha, Sourav; Guo, Jiachen; Park, Chanwook; Lu, Ye; Batley, Reza; Liu, Wing Kam (Elsevier, 2025)
Spatial variation of microstructure is an integral feature of almost all forms of metal additive manufacturing processes. This directional dependence makes part qualification extremely difficult. Using computational models with complex constitutive laws to bypass trial and error requires accurately identifying several model parameters with limited data. This article presents a systematic approach to using limited characterization data to identify crystal plasticity (CP) material law parameters and apply it to predict orientation and scan strategy dependence of the mechanical properties of laser powder bed fusion IN 625 tensile coupons. This work applies two methods, a higher-order proper generalized decomposition (HOPGD) and a novel interpolating neural network (INN), as surrogates of the full field model, and discusses their comparative performances. Furthermore, the article goes through the details of the adaptive sampling strategy to efficiently use offline databases and how to correctly approximate the microstructure representation from characterization data. The work demonstrates that the INN differentiable surrogate model requires a small set of offline data to effectively calibrate multiple CP parameters without resorting to expensive genetic algorithms for calibration. A computational model calibrated using INN can predict mechanical responses closer to the experiments compared to HOPGD. The authors have applied HOPGD to build a model to win the National Institute of Standards and Testing (NIST) Additive Manufacturing Benchmark Challenge 2022, and the same set of experiments are used to perform the comparison.
Parametric Model Order Reduction for Structural Optimization of Fiber Composite Structures
Sanmugadas, Varakini; Agarwal, Mayank; Borwankar, Pranav; Kapania, Rakesh K. (American Institute of Aeronautics and Astronautics, 2025-01-10)
This research addresses the inherent computational demand of metaheuristic optimization techniques by developing a novel optimization framework that uses parametric model order reduction (PMOR). The improvement in efficiency is demonstrated using the buckling optimization of variable-angle tow (VAT) composite panels, as navigating the complex design space of VAT composite structures with global optimization techniques requires a substantial number of function evaluations. A new affine summation relationship conducive to PMOR was derived for its finite element stiffness matrix using the concept of lamination parameters and material invariant matrices, and principal component analysis (PCA) was used to extract the reduced basis vectors. Particle swarm optimization was conducted using a full-order model (FOM) and a reduced-order model. PMOR-based optimization exhibited a 0.55% relative error in the optimal objective value compared to the FOM analysis and exhibited a similar convergence history. It was observed that the optimization time was reduced by 93% by the novel affine decomposition alone, but PMOR achieved a significant reduction of 99.8% in memory requirements compared to the FOM. As the affine-decomposition-based assembly of the FOM is not feasible for large problems, PMOR functions as an enabling tool for leveraging the improvement offered by it.
Resonance widths, chaotic zones, and transport in cislunar space
Rawat, Anjali; Kumar, Bhanu; Rosengren, Aaron J.; Ross, Shane D. (2025-08-14)
Lunar mean-motion resonances (MMRs) significantly shape cislunar dynamics beyond GEO forming stable-unstable pairs, with corresponding intermingled chaotic and regular regions. The resonance zone is rigorously defined using the separatrix of unstable resonant periodic orbits surrounding stable quasi-periodic regions. Our study leverages the restricted three-body problem to estimate the (stable) resonance widths and (unstable) chaotic resonance zones of influence of the 2:1 and 3:1 MMRs across various Jacobi constants, employing a Poincar´e map at perigee and presenting findings in easily interpretable geocentric orbital elements. An analysis of the semi-major axis versus eccentricity plane reveals broader regions of resonance influence than those predicted by semi-analytical models based on the perturbed Kepler problem. A comparison with observed spacecraft in these regions is made, showing excellent agreement.
Investigation of Interior Mean Motion Resonances and Heteroclinic Connections in the Earth-Moon System
Kumar, Bhanu; Rawat, Anjali; Rosengren, Aaron J.; Ross, Shane D. (International Astronautical Federation, 2024-10)
Understanding the dynamical structure of cislunar space beyond geosynchronous orbit is of significant importance for lunar exploration, as well as for design of high Earth-orbiting mission trajectories in other contexts. A key aspect of these dynamics is the presence of mean motion resonances, as heteroclinic connections between unstable resonant orbits are fundamental to changing and understanding the evolution over time of a spacecraft’s semimajor axis. In this paper, we first compute and analyze several important resonant orbit families within the Earth-Moon system based on the planar circular restricted 3-body problem. Focusing on interior resonances 4:1, 3:1, and 2:1, we identify a number of bifurcations of these resonances’ periodic orbit families. Once the aforementioned orbits are computed, we then compute their stable and unstable manifolds using an osculating orbit perigee Poincaré map. Carrying out these computations across a range of energy values, we are able to characterize the range of naturally attainable semimajor axis values for future distant Earth-bound or lunar spacecraft missions.
Heat Treatment Effect on the Corrosion Resistance of 316L Stainless Steel Produced by Laser Powder Bed Fusion
Sangoi, Kevin; Nadimi, Mahdi; Song, Jie; Fu, Yao (MDPI, 2025-01-04)
This study explores the effect of heat treatment on the microstructural characteristics and corrosion resistance of 316L stainless steels (SSs) produced via laser powder bed fusion (L-PBF), focusing on anisotropic corrosion behavior—a relatively less explored phenomenon in LPBF 316L SSs. By systematically analyzing the effects of varying heat treatment temperatures (500 °C, 750 °C, and 1000 °C), this work uncovers critical correlations between microstructural evolution and corrosion properties. The findings include the identification of anisotropic corrosion resistance between horizontal (XY) and vertical (XZ) planes, with the vertical plane demonstrating higher pitting and repassivation potentials but greater post-repassivation current densities. Furthermore, this study highlights reductions in grain size, dislocation density, and melt pool boundaries with increasing heat treatment temperatures, which collectively diminishes corrosion resistance. These insights advance the understanding of processing–structure–property relationships in additively manufactured metals, providing practical guidelines for optimizing thermal post-processing to enhance material performance in corrosive environments.
Monitoring Wind and Particle Concentrations Near Freshwater and Marine Harmful Algal Blooms (HABs)
Bilyeu, Landon; Gonzalez-Rocha, Javier; Hanlon, Regina; AlAmiri, Noora; Foroutan, Hosein; Alading, Kun; Ross, Shane D.; Schmale, David G. III (Royal Society of Chemistry, 2023-10-05)
Harmful algal blooms (HABs) are a threat to aquatic ecosystems worldwide. New information is needed about the environmental conditions associated with the aerosolization and transport of HAB cells and their associated toxins. This information is critical to help inform our understanding of potential exposures. We used a ground-based sensor package to monitor weather, measure airborne particles, and collect air samples on the shore of a freshwater HAB (bloom of predominantly Rhaphidiopsis, Lake Anna, Virginia) and a marine HAB (bloom of Karenia brevis, Gulf Coast, Florida). Each sensor package contained a sonic anemometer, impinger, and optical particle counter. A drone was used to measure vertical profiles of windspeed and wind direction at the shore and above the freshwater HAB. At the Florida sites, airborne particle number concentrations (cm−3) increased throughout the day and the wind direction (offshore versus onshore) was strongly associated with these particle number concentrations (cm−3). Offshore wind sources had particle number concentrations (cm−3) 3 to 4 times higher than those of onshore wind sources. A predictive model, trained on a random set of weather and particle number concentrations (cm−3) collected over the same time period, was able to predict airborne particle number concentrations (cm−3) with an R squared value of 0.581 for the freshwater HAB in Virginia and an R squared value of 0.804 for the marine HAB in Florida. The drone-based vertical profiles of the wind velocity showed differences in wind speed and direction at different altitudes, highlighting the need for wind measurements at multiple heights to capture environmental conditions driving the atmospheric transport of aerosolized HAB toxins. A surface flux equation was used to determine the rate of aerosol production at the beach sites based on the measured particle number concentrations (cm−3) and weather conditions. Additional work is needed to better understand the short-term fate and transport of aerosolized cyanobacterial cells and toxins and how this is influenced by local weather conditions.
Uncertainty Quantification in Data Fusion Classifier for Ship-Wake Detection
Costa, Maice; Sobien, Daniel; Garg, Ria; Cheung, Winnie; Krometis, Justin; Kauffman, Justin A. (MDPI, 2024-12-14)
Using deep learning model predictions requires not only understanding the model’s confidence but also its uncertainty, so we know when to trust the prediction or require support from a human. In this study, we used Monte Carlo dropout (MCDO) to characterize the uncertainty of deep learning image classification algorithms, including feature fusion models, on simulated synthetic aperture radar (SAR) images of persistent ship wakes. Comparing to a baseline, we used the distribution of predictions from dropout with simple mean value ensembling and the Kolmogorov—Smirnov (KS) test to classify in-domain and out-of-domain (OOD) test samples, created by rotating images to angles not present in the training data. Our objective was to improve the classification robustness and identify OOD images during the test time. The mean value ensembling did not improve the performance over the baseline, in that there was a –1.05% difference in the Matthews correlation coefficient (MCC) from the baseline model averaged across all SAR bands. The KS test, by contrast, saw an improvement of +12.5% difference in MCC and was able to identify the majority of OOD samples. Leveraging the full distribution of predictions improved the classification robustness and allowed labeling test images as OOD. The feature fusion models, however, did not improve the performance over the single SAR-band models, demonstrating that it is best to rely on the highest quality data source available (in our case, C-band).
In-flight measured propulsion mass flow and thrust on aircraft
Lowe, K. Todd; Byun, Gwi Bo; Krejmas, Albert (2024-10-22)
An aircraft includes a gas turbine engine and an optically-based measurement system. The gas turbine engine is configured to ingest a first mass flow and to exhaust a second mass flow. The optically-based measurement system is configured to determine the first and second mass flows in response to performing an imaging process on the gas turbine engine.
ALMO: Active Learning-Based Multi-Objective Optimization for Accelerating Constrained Evolutionary Algorithms
Singh, Karanpreet; Kapania, Rakesh K. (MDPI, 2024-10-31)
In multi-objective optimization, standard evolutionary algorithms, such as NSGA-II, are computationally expensive, particularly when handling complex constraints. Constraint evaluations, often the bottleneck, require substantial resources. Pre-trained surrogate models have been used to improve computational efficiency, but they often rely heavily on the model’s accuracy and require large datasets. In this study, we use active learning to accelerate multi-objective optimization. Active learning is a machine learning approach that selects the most informative data points to reduce the computational cost of labeling data. It is employed in this study to reduce the number of constraint evaluations during optimization by dynamically querying new data points only when the model is uncertain. Incorporating machine learning into this framework allows the optimization process to focus on critical areas of the search space adaptively, leveraging predictive models to guide the algorithm. This reduces computational overhead and marks a significant advancement in using machine learning to enhance the efficiency and scalability of multi-objective optimization tasks. This method is applied to six challenging benchmark problems and demonstrates more than a 50% reduction in constraint evaluations, with varying savings across different problems. This adaptive approach significantly enhances the computational efficiency of multi-objective optimization without requiring pre-trained models.
Spin Aerodynamic Modeling for a Fixed-Wing Aircraft Using Flight Data
Gresham, James L.; Simmons, Benjamin M.; Hopwood, Jeremy W.; Woolsey, Craig A. (American Institute of Aeronautics and Astronautics, 2024-01)
Novel techniques are used to identify a nonlinear, quasi-steady, coupled, spin aerodynamic model for a fixed-wing aircraft from flight-test data. Orthogonal phase-optimized multisine inputs are used as excitation signals while collecting spinning flight data. A novel vector decomposition of explanatory variables leads to an elegant model structure for spin flight data analysis. Results show good agreement between model predictions and validation flight data. This effort is motivated by interest in developing a flight termination system for a fixed-wing unmanned aircraft that controls a descending spiral trajectory flight path toward a designated impact area. While investigating the feasibility of a robust control method to guide the spinning trajectory, it was helpful to compare a level flight dynamic model with one of the aircraft dynamics and control authority in the neighborhood of a stable, oscillatory spin. In this paper, a nominal flight aerodynamic model is developed and compared to the stall spin model and the spin model outperforms the nominal model for spinning flight.
Remote Uncorrelated Pilot Input Excitation Assessment for Unmanned Aircraft Aerodynamic Modeling
Gresham, James L.; Simmons, Benjamin M.; Fahmi, Jean-Michel W.; Hopwood, Jeremy W.; Woolsey, Craig A. (American Institute of Aeronautics and Astronautics, 2023-05)
A Study of the Wind Sensing Performance of Small Pusher and Puller Hexacopters
González-Rocha, Javier; Sharma, Prashin; Atkins, Ella; Woolsey, Craig A. (American Institute of Aeronautics and Astronautics, 2023-09)
This paper presents a study comparing the performance of puller and pusher multirotors inferring time-varying wind velocity fluctuations from vehicle motion in hovering flight. For this analysis, linear models approximating the closed-loop airframe dynamics of pusher and puller multirotors were characterized from system identification experiments. The identified linear models were then used to synthesize a state observer for puller and pusher configurations. To validate wind estimation results based on vehicle motion, field experiments were performed in which the multirotors were stationed in hover above the ground at the center of a sensor array consisting of four sonic anemometers arranged in a tetrahedron configuration. Results from validation experiments show that puller and pusher hexacopters have comparable performance measuring wind velocity, but the pusher hexacopter platform was found to resolve time-varying wind fluctuations more accurately based on frequency-domain analysis of coherence and phase lag. The ease with which model-based estimation can be implemented for puller and pusher aircraft’s, further support the use of multirotors to infer wind velocity variations in the lower atmosphere.
Port-Hamiltonian Flight Control of a Fixed-Wing Aircraft
Fahmi, Jean-Michel W.; Woolsey, Craig A. (IEEE, 2022-01)
This brief addresses the problem of stabilizing steady, wing level flight of a fixed-wing aircraft to a specified inertial velocity (speed, course, and climb angle). The aircraft is modeled as a port-Hamiltonian system and the passivity of this system is leveraged in devising the nonlinear control law. The aerodynamic force model in the port-Hamiltonian formulation is quite general; the static, state feedback control scheme requires only basic assumptions concerning lift, side force, and drag. Following an energy-shaping approach, the static state feedback control law is designed to leverage the open-loop system’s port-Hamiltonian structure in order to construct a control Lyapunov function. Asymptotic stability of the desired flight condition is guaranteed within a large region of attraction. Simulations comparing the proposed flight controller with dynamic inversion suggest it is more robust to uncertainty in aerodynamics.
Experimental Validation of Port-Hamiltonian-Based Control for Fixed-Wing Unmanned Aircraft
Fahmi, Jean-Michel W.; Gresham, James L.; Woolsey, Craig A. (American Institute of Aeronautics and Astronautics, 2023-06)
A Structure-Inspired Disturbance Observer for Finite-Dimensional Mechanical Systems
Chen, Ying-Chun; Woolsey, Craig A. (IEEE, 2024-03)
This article describes a disturbance observer (DO) design for systems whose dynamics are piecewise differentiable and satisfy certain structural conditions. Provided a Lipschitz continuity condition holds with a sufficiently small Lipschitz constant—a condition that is implied by “sufficiently slow” dynamics—the observer ensures local ultimate boundedness of the disturbance estimate error, which converges exponentially to a positively invariant set whose size can be made arbitrarily small. This observer is appropriate for finite-dimensional mechanical systems. We demonstrate the design in two examples—a tutorial example of a nonlinear mass-damper-spring system and a practical example of an experimental underwater vehicle.

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