Aerospace Engineering Commons^™

Numerical Investigation Of Rotor-Gust Acoustic Interactions Using The Overflow Cfd Solver, Jordan Mills

Doctoral Dissertations and Master's Theses

The rapid expansion of Urban Air Mobility (UAM) necessitates high-fidelity modeling to predict and mitigate the noise signatures of electric vertical take-off and landing (eVTOL) aircraft within dense urban landscapes. A critical unknown in community-noise certification is the aeroacoustic response of rotors to unsteady inflow conditions. This research addresses this gap by investigating the aerodynamic and acoustic behavior of a representative rotor subjected to time-harmonic inflow disturbances. By establishing a robust numerical framework, this thesis quantifies the relationship between periodic atmospheric gusts and their impact on rotor performance, unsteady blade loading, and subsequent sound radiation. The research consists of a …

Effect Of Nozzle Pressure Ratio On Thrust And Flow Behavior In A Supersonic De Laval Nozzle, Esha Jain

Doctoral Dissertations and Master's Theses

Supersonic nozzles operate across a range of flow regimes. While an ideally expanded condition yields optimal thrust, practical propulsion systems rarely operate at this design point due to variations in altitude and engine operating conditions. As a result, nozzles frequently operate in off-design conditions. In overexpanded regime, where the exit pressure is lower than the ambient pressure, shock-induced separation may occur within the divergent section of the nozzle, potentially degrading nozzle performance. Understanding the aerodynamic behavior of nozzles operating under off-design conditions is therefore important for improving propulsion system performance and stability. In particular, direct thrust measurements provide a key …

Intelligent Flight Control Systems Using Adaptive Deep Neural Networks And Concurrent Learning-Based Design Methods, Maddox C. Morrison

Doctoral Dissertations and Master's Theses

This thesis investigates deep neural network (DNN)-based adaptive control strategies for unmanned aerial vehicles (UAVs) operating under aerodynamic uncertainty and complex actuator dynamics.

The first contribution presents a control strategy employing a concurrent learning (CL)-based DNN training algorithm, which combines online adaptive DNN weight adaptation with offline batch-like training updates using a recorded data stack. The analysis focuses on the closed-loop performance improvements resulting from the use of optimum CL data-selection algorithms, which ensure that the recorded data stack maintains sufficient data diversity to provide a statistically meaningful representation of the operating conditions using a reduced data set. Specifically, this …

Safety-Aware Trajectory Generation For Increased Autonomy In Advanced Air Mobility, Edison Alberto Martinez Samaniego

Doctoral Dissertations and Master's Theses

Advanced Air Mobility (AAM) envisions highly automated aircraft that will enable short and medium range transportation. Unlike conventional aviation, these vehicles are expected to operate closer to populated areas and with increased levels of autonomy, making safe operation under abnormal or degraded conditions a critical requirement. Failures or performance degradation can reduce the maneuvering capability of an aircraft, causing trajectories planned under nominal conditions to become dynamically unfeasible.

This thesis presents a trajectory generation and replanning framework designed to maintain safe and feasible flight under reduced flight envelope conditions for a lift+cruise eVTOL aircraft. A unified control architecture based on …

Quantification Of The Depth-Of-Field In A Self-Aligned Focusing Schlieren System, Alexander Ephraim

Doctoral Dissertations and Master's Theses

This thesis investigates self-aligned focusing schlieren (SAFS) as a step toward future volumetric and quantitative measurements of three-dimensional compressible flows. Conventional schlieren imaging provides valuable visualization of density gradients, but it records only a line-of-sight projection and therefore does not directly resolve the spatial distribution of structures through the depth of the flowfield. SAFS addresses part of this limitation by introducing depth sensitivity, but its depth response has not been well characterized quantitatively. To help lay the foundation for future volumetric and quantitative SAFS methods, this work addresses two related problems. First, calibrated quantitative schlieren imaging is applied to an …

Adaptive Methods Of Resident Space Object Identification For Space Situational Awareness, Evan Pavetto-Stewart

Doctoral Dissertations and Master's Theses

One of the fundamental tenets of Space Situational Awareness (SSA) is the detection and sub sequent identification of Resident Space Objects (RSOs) within unresolved optical space imagery. This function is vital to the documentation and tracking of RSOs in their respective operational orbits, knowledge that is necessary for collision avoidance efforts and Space Domain Awareness (SDA) applications. In previous work, development was begun on a MATLAB program called RSOID to fulfill this purpose by accepting a collection (or ’collect’) of unresolved imagery and outputting a sequence of RSO locations (called a ’tracklet’) that can be used to determine the RSO’s …

Evaluating Uav Visual-Inertial Odometry Trajectory Error And Feature-Level Metrics Over Repetitive Floor Patterns, Anass El Mekkoussi

Doctoral Dissertations and Master's Theses

Visual-Inertial Odometry (VIO) is a widely used state estimation technique for Uncrewed Aerial Vehicle (UAV) navigation in environments where Global Navigation Satellite System (GNSS) signals are unavailable. VIO systems that rely on visual feature tracking are susceptible to performance degradation when operating over surfaces containing repetitive visual textures, where visually similar features can produce ambiguous correspondences that introduce errors into the trajectory estimate. Despite the prevalence of repetitive textures in indoor UAV operating environments such as warehouses, manufacturing facilities, and infrastructure corridors, the specific impact of different repetitive pattern geometries on per-surface VIO accuracy has received limited systematic study, and …

Modeling And Autonomous Control Systems For A Delivery Drone Application, Dana Helal Alnuaimi

Theses

This thesis focuses on the development of a multi-drone navigation and control system, aiming to enhance payload capacities beyond the limits of single-drone systems. By integrating multiple drones to work collaboratively as a unit, this study addresses the challenges associated with lifting and transporting heavier payloads.

The primary objective is to design and evaluate a multi-drone system capable of working in tandem to transport larger payloads efficiently. The research aims to develop robust control algorithms, supported by system identification for dynamic modeling, and navigation strategies to enable effective coordination between drones.

The study employs a combination of simulation and real-world …

Artificial Intelligence In Human Spaceflight Safety-Critical Systems: A Requirements Framework For Ai-Enabled Computer-Based Control Systems, Liz Bosch

Doctoral Dissertations and Master's Theses

Currently, there is no governing standard that addresses the safe integration of AI into computer-based control systems (CBCS) in human spaceflight. The computer-based control expectations of those safety-critical systems on the International Space Station (ISS) are captured in SSP 50038, Computer-Based Control System Safety Requirements, with one caveat: the standard was not designed with AI applications in mind. SSP 50038 does not cover the probabilistic behavior, opacity, and training-data dependency of modern AI/ML systems. The objectives of this work is to develop an AI taxonomy relevant to safety characteristics (determinism, transparency, data dependency, failure predictability), systematically map AI characteristics against …

Reinforcement Learning - Driven Satellite Attitude Recovery: Unknown Faults, Simulation-To-Processor In Loop, Chinmay Mirji, Saeed A Ahmadi

Student Research Symposium (SRS)

Conventional attitude control algorithms often degrade when faced with actuator faults, sensor noise, or system uncertainties. This work presents a reinforcement-learning (RL) framework for satellite attitude recovery under unknown failures, focusing on real-time deployment through a processor-in-the-loop (PIL) setup. A continuous-control DDPG agent is trained in a high-fidelity Python/Basilisk simulation environment, where domain randomization captures variations in inertia, external torque, and actuator limitations to promote robust policy learning.

Conceptual Exploration Of Film Cooling Techniques In Hydrogen Gas Turbines, Wen Wu

Student Research Symposium (SRS)

Film cooling is a key heat transfer technique that protects gas turbine blades from extreme temperatures, allowing higher turbine inlet temperatures and greater thermal efficiency. It works by ejecting a thin layer of cooler air from the compressor through small holes onto the hot surface, creating a protective film layer that lowers surface temperature and reduces thermal stress on blades. This process is vital in power generation and propulsion systems that operate near material limits. With growing emphasis on sustainability, film cooling has gained new importance in hydrogen-fueled gas turbines. Hydrogen combustion creates water vaper and low-density exhaust gases, which …

Evaluating Runtime Monitoring For Reinforcement Learning-Based Flight Control, Andrew Zubyk

Doctoral Dissertations and Master's Theses

Ensuring safety in adaptive flight controls systems is an ongoing challenge in aviation, especially as advancements in artificial intelligence and machine learning (AI/ML) trend upwards. Reinforcement learning is becoming more common in aerospace applications due to the ability to improve these models through training.  While models such as reinforcement learning enable controllers to learn complex behaviors from interaction with the environment, their unpredictability in novel or disturbed conditions raises severe concerns in safety-critical domains. This research investigates the integration of runtime monitoring, a real-time assurance technique, with reinforcement learning-based flight controllers to ensure safety and reliability during flight. By supervising …

Slender And Nonslender Delta Wing Simulation And Analysis, Aashish Gyawali, Brinda Bhattarai, Nishesh Bista, Sundeep Rao Dr

Journal of Aviation Technology and Engineering

Stability, controllability, and maneuverability are critical factors for aircraft with short takeoff and landing distances, such as modern fighter aircraft and unmanned aerial vehicles. Delta wings are commonly employed in these aircraft due to their efficient aerodynamics, enabling high maneuverability, and performance at both low and high speeds. Nonslender wings are used for low-speed performance and agility, while slender wings offer reduced drag and are suited for high-speed operations. In flight, an aircraft encounters different airflow patterns including vortices that circulate from the higher-pressure lower side of the wing to the lower-pressure upper side, contributing to lift generation. However, as …

Initial Development Of Cooperative Aerial And Ground Vehicles Experimental Testbed, Javier S. Robinson, Morad Nazari

Beyond: Undergraduate Research Journal

Heterogeneous multi-agent systems represents a growing area of research in autonomous vehicles, which involves the real-time cooperation of vehicles operating under different roles or dynamical mod- els. While there is plenty of theoretical work, the existing experimental research focuses on cooperation between physically identical vehicles. This is likely because testing heterogeneous vehicles naturally in- volves more complicated dynamics and communication frameworks to ensure compatibility. The goal of the Cooperative Aerial and Ground Vehicles Experimental (CAGE) testbed is to develop an experimen- tal testbed that involves cooperation of two distinct vehicle models: multiple Crazyflie 2.1 quadcopter drones and at least one …

Mechanical Performance Of Equilateral Triangular Lattices: The Role Of Nodal Fillets, Fakhreddin Emami, Andrew J. Gross

Faculty Publications

Triangular lattices are widely employed for their high strength to weight ratios, yet their mechanical performance is sensitive to geometric features, particularly the nodal geometry. This study investigates the influence of nodal geometry on the mechanical behavior of equilateral triangular lattices across a broad range of relative densities using high fidelity finite element simulations. We characterize the elastic properties, and strength limits as functions of fillet radius. Our results confirm the expected trend that, in stretching-dominated lattices with low relative density, the introduction of fillets reduces both stiffness and buckling resistance. In contrast, at higher relative densities, filleted nodes can …

A Tangible Modeling Language For Systems Engineering, Maissane Aik

Student Research Symposium (SRS)

With the continuous growth of technology and system complexity, systems engineering plays a crucial role in ensuring performance, adaptability, and clarity across interconnected processes. Yet, as modeling tools become increasingly abstract and dependent on digital infrastructures, they have drifted away from the tangible, intuitive ways humans naturally think, communicate, and design. Current modeling approaches often require engineers to navigate numerous diagrams and layers of abstraction. To address the gap, this research will focus on creating a novel systems engineering language that reintroduces tangibility into system modeling. The proposed language will represent system elements as modular blocks that capture structure, behavior, …

Computational Study Of Slosh Dynamics And Active Slosh Damping In Spacecraft Propellant Tanks Equipped With A Magneto Active Propellant Management Device, Priyanshu Savaliya

Student Research Symposium (SRS)

The management of liquid propellant in microgravity remains one of the longest-standing issues in spacecraft design. Traditional passive damping techniques using baffles or diaphragms tend to add structural mass and complexity. The current study proposes a novel active slosh damping device which dynamically adjusts the rheology of the damper fluid using electromagnets, simulated in ANSYS Fluent, to actively manage and dampen slosh in an oscillatory excited cylindrical propellant tank. The electromagnetic fields are modulated through a User-Defined Function (UDF) that responds to force feedback simulated in real time via virtual load cells. In Fluent, the interface dynamics of xenon, fuel, …

Task-Based Analysis Of Augmented Reality In Collaborative Robotic Programming For Manufacturing Assembly, Medhavi Kamran, Snehesh Shrestha, Vinh Nguyen

Michigan Tech Publications

Augmented Reality (AR) is often promoted as a solution to the cognitive and physical demands of traditional Teach Pendant (TP) programming for collaborative robots. Although prior work has suggested advantages of the AR interface, many evaluations have been limited in scope and may not fully represent the complexities of real-world manufacturing tasks. This study compares the performance of an AR interface to that of a standard TP interface for manufacturing assembly tasks of varying difficulty. In a between-groups study, one group of operators completed standardized assembly tasks using the TP interface, while a separate group used the AR interface instead. …

An Ambient Acoustic Ice-Fracturing Dataset Taken In Shallow Freshwater, John Case, Andrew Barnard, Daniel Brown

Michigan Tech Publications

This paper describes an acoustic dataset collected on a frozen shallow freshwater lake between February and March of 2024. This collection took place over one full week on Portage Lake in the Upper Peninsula of Michigan, USA. The first sub-dataset consists of ambient ice and environmental noises collected by an array of hydrophones, microphones and geophones placed below, above and on the ice respectively. The second sub-dataset consists of instrumented force hammer impacts at a series of locations on the the ice with the corresponding response at each acoustic sensor. All acoustic data were recorded at a sample rate f …

Decentralized Q-Learning Supervisory Control For Coordinated Multi-Loop Tuning In Pump Stations, David Brattley, Wayne Weaver

Michigan Tech Publications

This paper introduces a reinforced learning-based supervisory control architecture that oversees multiple Recursive Least Squares (RLS) based self-tuning pump controllers and determines when each loop is permitted to adapt its gains. The supervisor learns adaptation policies that minimize interaction between loops while preserving responsiveness to changing hydraulic conditions. A two-loop pump station simulation is used to evaluate performance under product changes and transient flow disturbances. The results show that the supervisory layer reduces the number of simultaneous adaptation events by over 70%, leading to a 32% lower pressure-tracking error and 45% fewer gain-induced oscillations compared to conventional independent adaptive control. …