Scholarly Works, Aerospace and Ocean Engineering

Permanent URI for this collection

https://hdl.handle.net/10919/23164

Research articles, presentations, and other scholarship

Browse

Now showing 1 - 20 of 166

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)
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.
A Maneuvering Model for an Underwater Vehicle Near a Free Surface—Part II: Incorporation of the Free-Surface Memory
Battista, Thomas; Valentinis, Francis; Woolsey, Craig A. (IEEE, 2023-07)
Using energy-based modeling techniques, we propose a nonlinear, time-dependent, parametric motion model for an underwater vehicle maneuvering near an otherwise undisturbed free surface. By augmenting the system Lagrangian used to derive Kirchhoff's equations for a rigid body moving through an unbounded fluid, we directly incorporate the free surface into the derivation of the equations of motion. This is done using a free-surface Lagrangian , which accounts for the instantaneous energy stored within the free surface due to an impulsive vehicle motion as well as fluid memory effects. The system Lagrangian then enables us to derive the six-degree-of-freedom nonlinear equations of motion using the Euler–Lagrange equations. The model structure is similar to standard maneuvering models for surface ships, although additional complexities are present since the hydrodynamic parameters are shown to depend on the vessel position and orientation relative to the free surface. For the proposed model, the vessel motion is unrestricted. This is in contrast to traditional seakeeping models, which use convolution integrals to incorporate memory effects for a vessel, which experiences only small perturbations from steady, forward motion. The proposed motion model is amenable to real-time simulation, design performance analysis, and nonlinear control design. Other important hydrodynamic effects due to viscous flow, for example, may then be incorporated into a robust, nonlinear, closed-loop control system as lumped parameter effects or model uncertainties.
A Maneuvering Model for an Underwater Vehicle Near a Free Surface—Part III: Simulation and Control Under Waves
Valentinis, Francis; Battista, Thomas; Woolsey, Craig A. (IEEE, 2023-07)
This article incorporates free-surface and ambient wave effects into a nonlinear parametric model. Subsequently, its use is demonstrated via simulation of a scale model submarine maneuvering under the control of a nonlinear depth-keeping control system in a seaway. An energy-based model is presented, which represents the underactuated submarine in a free-surface-affected state. This model is then used to synthesize a control law using port-Hamiltonian theory and interconnection and damping assignment passivity-based control. The Lyapunov analysis is used to study the stability of the closed-loop system, and a simulation-based demonstration illustrates the performance of the control law. The results demonstrate that a closed-loop nonlinear controller is able to improve the quality of near-surface depth keeping by automatically compensating for parasitic effects in the hydrodynamics that can compromise depth-keeping performance during maneuvers.
On closed-loop vibrational control of underactuated mechanical systems
Tahmasian, Sevak; Woolsey, Craig A. (Springer Nature, 2022-01)
This paper discusses vibrational stabilization of a class of single-input, two degree-of-freedom mechanical systems. Considering two different control formulations—position-input and force-input—and both open- and closed-loop control, we find that the sets of attainable equilibrium positions for the unactuated coordinate are identical in every case. The subset of positions that are stabilizable, however, depends on the formulation. In general, the set of equilibria that can be stabilized using open-loop force-input is larger than the set that can be stabilized using open-loop position-input. And the use of feedback expands this stabilizable set even further. As examples, this paper presents the dynamic analysis, open- and closed-loop vibrational control, and the mechanics behind the stability of two underactuated systems, the Kapitza pendulum and a one-link horizontal pendulum.
A Tutorial Review of Indirect Wind Estimation Methods Using Small Uncrewed Air Vehicles
Ahmed, Zakia; Halefom, Mekonen H.; Woolsey, Craig A. (American Institute of Aeronautics and Astronautics, 2024-08)
Flight-Test System Identification Techniques and Applications for Small, Low-Cost, Fixed-Wing Aircraft
Simmons, Benjamin M.; Gresham, James L.; Woolsey, Craig A. (American Institute of Aeronautics and Astronautics, 2023-09)
This paper provides an overview of flight-test system identification methods applied in the Virginia Tech Nonlinear Systems Laboratory that focus on modeling small, inexpensive, fixed-wing aircraft controlled by a ground-based pilot. The general aircraft system identification approach is outlined with details provided on the flight-test facilities, experiment design methods, instrumentation systems, flight-test operations, data processing techniques, and model identification methods enabling small aircraft flight dynamics model development. Specific small aircraft system identification challenges are overcome, including low-cost control surface servo-actuators and instrumentation systems, as well as a greater sensitivity to atmospheric disturbances and limited piloting cues. Four recent system identification research advancements using the general system identification process are featured, including application of uncorrelated pilot inputs for remotely piloted aircraft, aero-propulsive model development for propeller aircraft, spin aerodynamic model development, and nonlinear dynamic modeling without mass properties information. Although this paper provides a summary of several research efforts, the core system identification approach is presented with sufficient detail to allow the methods to be readily adapted to other research efforts leveraging small, low-cost aircraft.
Nonlinear Dynamic Modeling for Aircraft with Unknown Mass Properties Using Flight Data
Simmons, Benjamin M.; Gresham, James L.; Woolsey, Craig A. (American Institute of Aeronautics and Astronautics, 2023-05)
Time Delay Mitigation in Aerial Telerobotic Operations Using Heterogeneous Stereo-Vision Systems
Sakib, Nazmus; Gahan, Kenneth C.; Woolsey, Craig A. (American Institute of Aeronautics and Astronautics, 2023-09)
This paper investigates the use of a heterogeneous stereo-vision system to mitigate the effects of time delays in a drone-based visual interface presented to a human operator. Time delays in the display for a telerobotic interface refer to the time difference between the operator’s input action and the corresponding visible outcome. In human/machine interfaces, time delays can arise due to computation, telecommunication, and mechanical limitations. These delays can degrade the performance of the human/machine system. A heterogeneous stereo-vision predictive algorithm is presented that can reduce the negative effects of time delays in the operator’s display. The heterogeneous stereo-vision system consists of an omnidirectional camera and a pan/tilt/zoom camera. Two predictive display setups were developed that modify the delayed video imagery that would otherwise be presented to the operator in a way that provides an almost immediate visual response to the operator’s control actions. The usability of the system is determined through human performance testing with and without the predictive algorithms. The results indicate that the predictive algorithm allows more efficient, accurate, and user-friendly operation.
A free surface corrected lumped parameter model for near-surface horizontal maneuvers of underwater vehicles in waves
Lambert, William; Miller, Lakshmi; Brizzolara, Stefano; Woolsey, Craig A. (Elsevier, 2023-06)
We provide theory and results obtained from the application of a 6-degree of freedom lumped parameter maneuvering model (LPMM) able to predict the maneuvering motions of underwater vehicles navigating at shallow depth below free surface waves. The parameters of the maneuvering model are identified using a combination of steady and unsteady captive maneuvers, simulated with high fidelity computational fluid dynamic (CFD) methods. This work focuses on correcting the LPMM, which is accurate for deep water motions, to account for free surface effects. A frequency domain strip theory method is used to account for changes in the added mass due to free surface proximity, to calculate memory forces, and to estimate wave excitation forces while a 3D time domain boundary element method is used to predict steady-state wave making forces. The result is a combined maneuvering and seakeeping model for underwater vehicles operating at shallow depths below the free surface. Near-surface horizontal zig-zag motion predictions in both calm water and under waves reveal the importance of including the free surface as vehicle trajectories at shallow depths differ substantially from those at deeper submergences for identical maneuvering inputs.
Development of a Peripheral–Central Vision System for Small Unmanned Aircraft Tracking
Kang, Changkoo; Chaudhry, Haseeb; Woolsey, Craig A.; Kochersberger, Kevin (American Institute of Aeronautics and Astronautics, 2021-09)
Two image-based sensing methods are merged to mimic human vision in support of airborne detect-and-avoid and counter–unmanned aircraft systems applications. In the proposed sensing system architecture, a peripheral vision camera (with a fisheye lens) provides a large field of view, whereas a central vision camera (with a perspective lens) provides high-resolution imagery of a specific target. Beyond the complementary ability of the two cameras and supporting algorithms to enable passive detection and classification, the pair forms a heterogeneous stereo vision system that can support range resolution. The paper describes development and testing of a novel peripheral–central vision system to detect, localize, and classify an airborne threat. The system was used to generate a dataset for various types of mock threats in order to experimentally validate parametric analysis of the threat localization error. A system performance analysis based on Monte Carlo simulations is also described, providing further insight concerning the effect of system parameters on threat localization accuracy.

Browse

Recent Submissions