Master of Sciences (Engineering), Case Western Reserve University, 2024, EECS - System and Control Engineering

Wire Arc Additive Manufacturing (WAAM) has emerged as a promising technology for producing metal parts, offering reduced lead times and costs compared to traditional methods. However, achieving optimal process parameters in WAAM and accurately predicting bead height remain challenging due to complex interactions between input variables and output characteristics. This thesis addresses the challenge of developing a machine learning regression model to predict the average bead height of single deposited beads, crucial for building simple and complex shapes in WAAM. The research investigates the relationship between four critical input parameters - Voltage, Wire Feed Speed (WFS), Travel Speed, Contact Tip to Work Distance (CTWD) - and their influence on bead dimensions in WAAM. A comprehensive experimental setup is employed, utilizing a custom-built WAAM 3D metal printer equipped with a gantry system and controlled by a Duet 3 controller. Steel wire ER70s-6 with a diameter of 0.9mm is used for printing, producing single beads with heights ranging from 2.5mm to 3.55mm. A total of 248 experiments are performed using the Arc-One Machine at Case Western Reserve University (CWRU) for the model training, which are then analyzed. A machine learning regression model is built using this dataset, with four inputs (Voltage, Travel Speed, Wire Feed Speed, Contact Tip to Work Distance) and two corresponding outputs (average bead height and variance of bead heights). Various analytical techniques were explored to predict the average bead height and its variance, leading to the adoption of the Gradient 18 Boosting regression model as the most effective approach. Two models, a forward model and an inverse model, were developed to predict WAAM parameters and outputs. The forward model predicts the average bead height and variance based on the input parameters (Voltage, Wire Feed Speed, Travel Speed, and Contact Tip to Work Distance), providing insights into how th (open full item for complete abstract)

Master of Science in Aerospace Systems Engineering (MSASE), Wright State University, 2024, Mechanical Engineering

The development of high order numerical schemes has been instrumental in advancing computational fluid dynamics (CFD), particularly for applications requiring high resolution of discontinuities and complex flow phenomena prevalent in high-speed flows. This thesis introduces the Pade-ENO scheme, a high-order method that integrates Essentially Non-Oscillatory (ENO) techniques with compact Pade stencils to achieve superior accuracy, up to 7th order, while maintaining stability in harsh environments. The scheme's performance is evaluated through benchmark tests, including the advection equation, Burgers' equation, and the Euler equations. For high Mach number flows, such as the sod shock tube the Pade-ENO method demonstrates its ability to resolve sharp gradients and discontinuities with no smoothing required. Numerical results highlight the scheme's robustness and its potential as a powerful tool for high-speed aerodynamic simulations, paving the way for future advancements in CFD modeling.

MS, University of Cincinnati, 2024, Engineering and Applied Science: Aerospace Engineering

In current liquid bipropellant rocket engines, a small amount of fuel and oxidizer is burnt in the gas generator and these hot gases drive the gas turbine which in turn powers the turbopumps. Turbopump generates high pressure difference which moments the propellants to the main combustion chamber. Any machine capable of producing rotational energy can replace the gas turbine and gas generator in a rocket engine to drive the pumps. This study focuses on proposing the use of Wankel rotary engine to drive the fuel and oxidizer pumps. Implementing the rotary engine that would run on pure liquid oxygen rather than air could replace the gas generator and gas turbine for powering the pumps. This results in reduction in the overall mass of the launch vehicle without compromising the performance. Here Rocket Propellant 1 (RP1) and Liquid Oxygen (LOX) are used as the propellants for a small thrust capacity second stage rocket engine. Initially, a method is developed to calculate design parameters of a rocket engine operating on the gas generator power cycle (open cycle) iterated over a range of thrust capacities. This includes calculations at system and component level, considering various pressure losses as the propellant flows through the system. Following this, a comparison between the proposed power cycle and the canonical engines is lead forth to present the performance superiority. Theoretical calculations show that for a specific range of operation parameters, launch vehicles powered by rocket engines running on the suggested power cycle can lift more payload than current capacity. Other potential benefits of using this technology are elucidated.

PhD, University of Cincinnati, 2024, Engineering and Applied Science: Aerospace Engineering

The move towards renewable energy has highlighted the need for large-scale, environmentally friendly energy storage solutions. Among these, the Supercritical Carbon Dioxide (sCO2) power cycle is emerging as a promising technology for advanced energy conversion. The effectiveness of such systems depends heavily on the compressor's performance. Using optimization-based methods, a multistage axial compressor has been designed, and its first stage has been built and tested experimentally. Through a series of detailed design iterations and optimization strategies, 3D CFD analyses, the compressor's geometric and operational parameters were fine-tuned to address the unique challenges posed by operation using CO2. Key findings highlight the successful implementation of design optimization that significantly reduces aerodynamic losses and improves the overall efficiency of the compressor stages. The optimized compressor demonstrates robust performance across a range of operating conditions, particularly focusing on improved stall margin, which emphasizes the potential of sCO2 technology in contributing to efficient and sustainable energy systems. Further detailed studies using CFD to analyze cavity effects in shrouded configurations, tip clearance effects, real gas effects, and Reynolds number effects were performed. Experimental validations, conducted at the University of Notre Dame Turbomachinery Laboratory, confirm the CFD predictions and showcase the practical viability of the compressor design and the approach used. This work not only advances the state-of-the-art in turbomachinery design for supercritical fluids but also lays a foundation for future research into the integration of sCO2 and real gas based compressors in renewable energy systems and industrial applications. The insights gained from this study underscore the critical importance of tailored design and optimization strategies in overcoming the thermophysical challenges associated (open full item for complete abstract)

Master of Science in Electrical Engineering, University of Dayton, 2024, Electrical and Computer Engineering

This paper presents the robotic control vision system used in Cold Spray (CS) repairs. The robot used, is a standard six axis degree of freedom (6DOF) arm to hold the CS system. Show the calibration techniques for the eye to hand vision system. Show how the pictures are analyzed with python code using OpenCV to identify the repairs, and plan the path of the CS. How an industry programmable logic controller (PLC) is used a mediator device between the computer and the robotic controller. The robot then runs a loop to go from repair to repair using the data interpreted by the vision system. This paper will also measure the accuracy of the points found and how accurate the point found are to the point transition to see if this falls in the tolerance of the cameras specifications.

MS, University of Cincinnati, 2024, Engineering and Applied Science: Aerospace Engineering

Imagine an aircraft wing with better performance, less fuel consumption, and a smaller span than the conventional wing. The concept of a ring wing, with its unique circular wing configuration, makes it possible. This thesis explores the concept of a ring wing, which has fascinated aviation enthusiasts for decades. Despite their intriguing potential, ring-wing planes face numerous design challenges, and little comprehensive research has been conducted to explore the various configurations and their performance in different flight conditions. This thesis aims to fill that gap by comprehensively analyzing the aerodynamic characteristics of ring-wing configurations and their performance in various flight conditions. To do this, we start by validating the conventional wing and then modifying it to create different configurations of ring and elliptical wings. These configurations are further modified by changing the sweep angle. Finally, we add a vertical structure to push the wing backward for aerodynamic reasons. We compare these wings with straight box wings with the same dimensional parameters to observe the difference in aerodynamic parameters. Additionally, we compare the performance of the wing configuration with maximum aerodynamic performance (among all the wing configurations in this thesis) with the performance of a combination of multiple small wings that add up to the same area as the big wing. The idea is to replace the one big wing configuration with two or more small wing configurations that add up to the same area to reduce the diameter. The results of this study will provide valuable insights into the feasibility of ring-wing aircraft. Specifically, the results will help determine whether ring-wing aircraft are a viable alternative to conventional aircraft.

MS, University of Cincinnati, 2024, Engineering and Applied Science: Aerospace Engineering

A major concern in the oil and gas industry is erosion corrosion which can cause catastrophic failure in pipelines. To monitor and prevent this failure, networks of acoustic emission sensors have been installed on pipelines to detect the presence of abrasive particles in the fluid flow. These abrasive particles damage the inside walls of the pipes through high-velocity impact. It would be advantageous to utilize the ultrasonic transducers in these existing monitoring systems to measure wall thickness. Two main roadblocks exist in utilizing these transducers for wall thickness measurements. First, these systems do not have a way of providing the typical excitation needed for ultrasonic measurements. To combat this issue, this thesis explores two different passive approaches: one that requires no purposeful excitation and another that utilizes acoustic emission from particle impact and fluid flow within the pipe. The second challenge in measuring wall thickness using existing transducers is the frequency range of these transducers which is much lower than what is typically used for ultrasonic time-of-flight thickness measurements. To address this problem, this thesis explores the sensitivity of transducers to the upper limits of their frequency range using a time-of-flight method. Additionally, for thinner-walled components which would require even higher frequencies, a resonant ultrasound spectroscopy method is explored. Experimental measurements using the different measurement modalities and passive excitation approaches are shown using multiple transducers. Several of the experimental combinations tested show good agreement with active measurements and show promise in determining wall thickness.

PhD, University of Cincinnati, 2024, Engineering and Applied Science: Aerospace Engineering

The pulsejet study investigates twelve configurations of the self-aspirated valved pulsejet, focusing on its operational mechanism. It begins by characterizing the engine's acoustic field with varied boundary conditions, revealing an extended effective acoustic length beyond the combustion chamber and tailpipe sections when the valve is open. Radial and lateral velocity fluctuations are hypothesized as causes for vortex structure generation. Analysis of reacting pulsejets using gasoline and ethanol fuels shows that the operating frequencies are unaffected by fuel type, with geometric arrangement as the primary frequency factor. Fluctuations in dynamic pressure, microphone readings, and thrust characterize engine stability, impacted by low-frequency modes and higher harmonics. Tailpipe length emerges as the key geometric factor for performance enhancement with the medium size being the optimal option. Pressure field analysis demonstrates shock wave generation during ignition, serving as an excitation mechanism. Notably, different configurations with varied operating frequencies can yield equivalent thrust, indicating that thrust production is not solely dependent on pressure rise during combustion, despite similar pressure rise observed across most cases. The second study investigates the impact of the equivalence ratio, inlet air temperature, confinement ratio, and exit boundary on the flame dynamics of a single-element, low-emission nozzle used in a multipoint lean direct injection (MLDI) combustion system. High-speed OH* chemiluminescence, combined with sound pressure measurements, is used to analyze the flame structure and its correlation to sound intensity. Three distinct flame types are identified: the V-flame, M-flame, and lifted-distributed flame. The V-flame, occurring at higher equivalence ratios, is associated with axial fluctuation modes and is coupled (in-phase) with the acoustic field, leading to higher sound intensity. Notably, the matching (open full item for complete abstract)

PhD, University of Cincinnati, 2024, Engineering and Applied Science: Aerospace Engineering

Aerial application of agrochemicals plays a crucial role in modern agriculture. Droplet size is a critical parameter, affecting both the efficacy of the chemicals and the risk of off-target motion, especially drift. However, the droplet size produced by the spray system in-flight is difficult to predict due to the many variables impacting the atomization process. To address various limitations with the current approach to determining droplet size, this dissertation presents the development of and justification for an in-flight droplet sizing sensor specifically designed for aerial application spray measurements. First, a study is undertaken to quantify errors in droplet size and drift estimates based on empirical modeling that does not account for the varying liquid physical properties of commonly used spray solutions. Existing wind tunnel atomization data is analyzed with commonly used drift modeling software. Key droplet size distribution statistics and near-field drift metrics for a commonly used blank solution of water with a non-ionic surfactant are compared to those from various active ingredient and adjuvant formulations. It was found that even relatively small changes in three liquid physical properties—dynamic surface tension, shear viscosity, and extensional viscosity—could result in large (over 50%) differences in both the volume contained in small droplets and near-field drift between the blank solution and the active ingredient formulations. The magnitude of these changes varied significantly depending on the sprayer configuration. Next, the development of a droplet sizing sensor based on the image-based particle shadow imagery (PSI) technique is presented to address the challenges related to predicting droplet size. Capable of real-time, in-flight measurements on commercially operating aircraft, the PSI sensor was designed to address the challenges related to predicting droplet size by simply measuring it during a spray application. A un (open full item for complete abstract)

MS, University of Cincinnati, 2024, Engineering and Applied Science: Aerospace Engineering

In recent years, rotating detonation combustors (RDCs) have come to the forefront of combustion research with their potential to revolutionize gas turbine combustion. The technology promises to drastically increase combustion efficiency, reduce combustor complexity/sizing, and achieve significant pressure rise across the combustor. A form of pressure gain combustion, rotating detonation combustors utilize detonative combustion to achieve combustion of reactants in a continuous but inherently unstable process. A detonation cell anchors within the combustor chamber and continuously rotates around the thrust axis, combusting fresh reactants as the wave travels, at supersonic velocities. This work explores a novel concept for an RDC which utilizes two separate air and fuel flows. Referred to as the pilot and core flows, a combusted core flow undergoes secondary combustion through a rotating detonation combustor acting as a pilot flameholder to impart additional energy. This approach hopes to not only improve the RDC operation space, but also to demonstrate the viability of an RDC as a device to detonatively combust reactants in two separate high-temperature flows. While the pilot flow is electrically heated, the core flow is heated via combustion to achieve higher temperatures, with the reduced oxygen content considered. This work focuses on the design and initial testing of a facility constructed to serve as a platform for investigating combustors with conditions beyond traditionally available laboratory flows. The facility, known as the Center Hill Detonation Laboratory (CDL) is located at the University of Cincinnati's Center Hill Research Facility, a satellite campus of the University of Cincinnati built for advanced research experiments. At the core of the facility's capabilities are the ability to provide multiple sources of high air mass flows, electric air heating, and a gaseous pre-burner system that can provide elevated core flow temperatures. Crucia (open full item for complete abstract)

Master of Computer Science (M.C.S.), University of Dayton, 2024, Computer Science

Predicting pilot performance is crucial for enhancing flight safety, efficiency and training effectiveness. This study explores the predictive capabilities of regression–based machine learning algorithms for pilot behavior during a standard rate turn maneuver. Temporal data was collected from 16 certified pilots using flight simulators under different virtual reality visual conditions. The aim was to accurately predict six target variables that represented both final standard rate turn, or task, performance and manual control inputs used by pilots to achieve such performance. These six target variables included: (1) final heading error; (2) final yaw rate; (3) final altitude error; (4) maximum positive aileron input; (5) maximum negative aileron input; (6) total aileron input. Six regression-based machine learning algorithms were employed based on top performers within the literature with respect to predicting piloting behavior and general human movement. These six algorithms included: (1) Random Forest; (2) Gaussian Process Regressor; (3) Gradient Boosting Regressor; (4) Linear Regressor; (5) Decision Tree; (6) k-Nearest Neighbor. Gaussian Process Regression performed the best followed closely by Random Forest, with single–output models performing equally well as multi-output models indicating weak correlations among the target variables. Four of the six target variables were found to be determined with high predictive accuracy temporally early on, while the final altitude error and the maximum positive aileron input required the longest temporal information to achieve the highest predictive accuracy. These findings support the usage of such algorithms in automated pilot training systems for providing early indicators in identifying pilots who may require additional guidance. Additionally, environmental factors such as visual richness, or visibility with respect to percentage of cloud cover, seem to affect pilot performance but do not significantly impact the pe (open full item for complete abstract)

Master of Science (M.S.), University of Dayton, 2024, Aerospace Engineering

Modern aircraft are overburdened by electrical systems, which are continually increasing their power demands. To generate power on aircraft flying in high speed regimes where high temperatures are imposed by viscous heating, solid state devices can be employed. The high temperature gradient across the thermal protection system of the aircraft creates an ideal environment for thermoelectric generator (TEG) application. The North American X-15 was chosen for its high speed fight profiles and wealth of information available. The fight profiles and geometry will be used to gather data in a more applied sense. One-dimensional codes have been written to model the system's performance over the course of a specified fight profile. Utilizing generic relations, the flow temperature was determined through radiative equilibrium methods, which was then fed to the remaining system. The performance of the system was evaluated over the X-15 high speed mission fight profile. The thicknesses of each component of the system were varied until an optimal range was found. The optimal values found were used as the basis for the remaining computational modeling and physical testing. A high-fidelity modeling effort has been completed to model both the high temperature flow and the transient thermoelectric generator operation. The high temperature flow model is solved in parallel with a conduction heat transfer model of the vehicle skin. This allows the flow and solid bodies to react to one another throughout the transient operation. The models are loosely coupled to a high-fidelity model of a thermoelectric generator. The specific TEG model has been constructed to represent a physical module that was obtained for the physical test articles. Accompanying the computational modeling, two physical test articles have been developed and studied. The first consists of a single TEG stack consisting of a skin material, the TEG, and a heat sink. The second test article includes multiple TEGs within s (open full item for complete abstract)

Master of Science, Miami University, 2025, Mechanical and Manufacturing Engineering

Reduced order modeling (ROM) methods, such as those based upon Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD), offer data-based turbulence modeling with potential applications for flow control. While these models are often cheaper than numerical approaches, their results require validation with source data. Within the literature, the metrics and standards used to validate these models are often inconsistent. Chabot (2014) produced a data-driven framework for validating these ROMs that used surrogate Markov models (SMMs) to compare how the system dynamics evolved rather than how any single metric evolved. These SMMs were constructed by clustering the flow data into different states of suitably similar flow fields, and the Markov model then mapped how likely each state was to transition into another. While this method was successful, there persisted an amount of uncertainty in how the outlier states within this clustering scheme were determined. Additionally, the study only examined the application of this procedure to POD-Galerkin ROMs. This study aims to tie the outlier state determination directly to the models' parent data. The study will also apply this procedure to ROMs generated from DMD to investigate how this framework's effectiveness carries over to different classes of ROMs.

Master of Sciences (Engineering), Case Western Reserve University, 2025, EMC - Mechanical Engineering

Particle Image Velocimetry (PIV) is a technique that allows velocity measurements of a 2D plane of a fluid flow by illuminating seeding particles in the fluid with a laser sheet. However, the use of laser is often costly, introduces complexity, and poses a challenge in near-wall measurements due to light scattering from surfaces. Particle shadow velocimetry (PSV) is a novel velocimetry technique with the potential of being a low-cost, laser-free alternative to the established PIV technique. It works by tracking shadows cast by the seeding particles on an illuminated background. Little is known about the accuracy of this technique validated against PIV. This study starts by characterization the performance of this technique, presents results from two experiments using both PIV and PSV on the same plane, and discusses its advantages and potential applications.

Master of Engineering, Case Western Reserve University, 2025, EMC - Aerospace Engineering

Analysis of three missions has been carried out with a set of three power and three propulsion systems to determine system synergy as well as to find an optimal system for each mission. The three missions are GTO to LLO, LMO, and Titan flyby. The three propulsion systems of analysis are Hall Effect Thrusters, Magnetoplasmadynamic Thrusters, and VASIMR Thrusters. The three power systems of analysis are Silicon photovoltaics, multi-junction photovoltaics, and nuclear reactors. The mission to Low Lunar Orbit has a maximum trip time of 8 weeks, and four systems are capable of achieving this result. Those systems are Hall-Nuclear, MPDNuclear, VASIMR-MJ, and VASIMR-Nuclear, the last of which achieves the target in just 26 days. The VASIMR-Nuclear system is also capable of bringing the most passengers with a total capacity of 255 people. The mission to LMO was limited to a maximum trip time of 9 months, and 1 system is capable of achieving this result. This system is again VASIMR-Nuclear, capable of bringing 31 people the LMO in 241 days, or about 8 months. The mission to Titan flyby using the VASIMR-Nuclear system is capable of bringing 54500 kg of dry mass to Titan flyby at 1000 km at 3.13 km/s relative to the planet.

Master of Science (M.S.), University of Dayton, 2024, Mechanical Engineering

The next generation of aircraft requires novel structural designs capable of enduring extreme multiphysics environments. Multiscale topology optimization – where macroscale properties and mesoscale topology are either concurrently or sequentially optimized – provides a compelling framework for addressing this complex structural design problem. In the sequential scheme utilized herein, targeted thermo-mechanical properties specified by a macroscale optimization are subsequently matched with a mesoscale unit cell topology and corresponding homogenized properties from a mesoscale optimization. Although this sequential approach provides greater design flexibility than its concurrent counterparts, a key drawback is significant computational expense. To mitigate this expense, this thesis introduces two novel and efficient mesostructure selection approaches, both predicated on jettisoning the computationally expensive mesoscale optimization. In its place, the proposed methods select – from a series of pre-existing designs generated prior to the macroscale optimization (thus adding no computational time) – the unit cell topology that best matches the targeted thermo-mechanical properites of the voxel. Our framework is first applied to a thermo-mechanically loaded Messerschmitt-Bolkow-Blohm beam, with the resulting designs exhibiting a compliance within 1% and a resistivity within 4% of the optimized solution, while simultaneously reducing computational burden from months to minutes. This framework is then applied to a thermally loaded bi-material ring problem, with an Inconel 718 ring encased by a carbon composite ring. The topology of the Inconel 718 ring was designed by the framework to accommodate the strain mismatch between the two materials caused by differing coefficients of thermal expansion. Experimental results indicate that the bi-material ring performed effectively and successfully accommodated the strain mismatch between the two rings. Overall, this research dem (open full item for complete abstract)

Doctor of Philosophy (Ph.D.), University of Dayton, 2024, Mechanical Engineering

As avionics become more power dense, electronic device cooling has become a significant barrier for aircraft integration. Mechanically pumped two-phase loops (MPTL) are a thermal subsystem with many desirable characteristics to address future aircraft heat loads. This dissertation has three main sections and uses numerical and experimental techniques to develop component technology and provide additional system characterization for MPTL transient operation. In the first section, a compressible volume accumulator is added to a MPTL to assess performance improvements for a single pulsed evaporator heat load. This study demonstrates that incorporation of compressible volume into a MPTL can provide near isothermal evaporator operation whereas a fixed-volume MPTL architecture cannot maintain isothermal requirements. The second study uses a validated numerical model to evaluate performance benefits provided by an advanced controls approach, model predictive control, compared to a traditional, proportional-integral control scheme for a single MPTL architecture. Multiple evaporator heat load profiles were applied using simulation, and the model predictive control scheme demonstrated improved isothermal operation compared to traditional control techniques. The third study evaluates system packaging considerations for MPTL compactness through assessment of large curvature ratio, 180° U-bend test articles. An electrical capacitance measurement device was used to examine hydraulic impacts on two-phase refrigerant flow for multiple two-phase flow patterns and U-bend geometries. Experimental results indicate the U-bend impacts the refrigerant upstream less than downstream and the analysis of x-location center of mass and the L2-norm do not provide sufficient data to support or refute current refrigerant re-development recommendations. In summary, each of these studies offer techniques to improve MPTL transient operation and compactness, which will provide benefit for future aircr (open full item for complete abstract)

Master of Science in Mechanical Engineering (MSME), Wright State University, 2024, Mechanical Engineering

An approach for the architecture optimization of emulator embedded neural networks is proposed. While the emulator embedded neural network has been shown to provide accurate predictions with suitable emulators, there is still a challenge regarding how to select the optimal hyperparameters of network architectures, such as, the number of neurons, layers, types of activation functions, etc. The selection of hyperparameters greatly affects the performance of the neural network model training both in terms of accuracy and efficiency. To address this challenge, this study proposes an algorithm that tests a range of hyperparameters and selects the best performing set. The algorithm compares network architectures using average cross-validation error and architecture size. Additionally, the algorithm implements Bayesian optimization to accelerate the hyperparameter selection process and leverages a database of benchmark analytical problems to better define the hyperparameter search space. The proposed method is demonstrated using analytical examples, an aerospace fracture mechanics design study, and a representative aerospace vehicle design study. It was found that the proposed algorithm was able to successfully select well-performing architectures from within the chosen search spaces. In comparison to the popular grid search algorithm, it found architectures of similar sizes and performance while testing less than half of the total number of architectures. The proposed algorithm was able to successfully avoid large architectures when the accuracy benefits were minimal compared to smaller architectures, saving both time and computational efficiency. The potential benefits of the algorithm when applied to aerospace design application are an increased confidence in the selected architecture, identification of best fit architectures with less dependence on experts' knowledge and experience, and reduction in time and computational efficiency when selecting an architecture.

Master of Science in Mechanical Engineering (MSME), Wright State University, 2024, Mechanical Engineering

In the constant search for more efficient engines, one approach to gain performance is to reduce the weight of the low pressure turbine (LPT) module. This module can account for up to 30% of the total engine weight [1], and a reduction in LPT weight results in clear gains to engine performance and a reduction in engine cost. High lift airfoils accomplish this weight reduction by each blade extracting a larger amount of work from the flow and thus requiring fewer blades to drive the compressor when compared to conventional blades. However, high lift LPT blades, quantified by a high Zweifel loading coefficient Zw>1.15, encounter increasing loss at low Reynolds numbers. Named Reynolds lapse, this effect is problematic if the engine must operate at high altitude cruise conditions such as the case with unmanned air vehicles. The two airfoils of this study, the L2FHW and the L3FHW, were designed to be front loaded and to demonstrate favorable low Reynolds number loss characteristics. Both airfoils were tested in the Transonic Turbine Cascade (TTC) at the Air Force Research Laboratory Building 18 Test Cell 21. The TTC is capable of high Mach number and low Reynolds number flow via independent control of each. Each airfoil was tested across a broad range of Mach numbers: exit Mach 0.78 down to 0.2 and exit Reynolds numbers from 23,000 to 201,000. Across each condition an exit total pressure traverse yielded the loss coefficient of the cascade at that condition. It was found that across all design exit Mach conditions, 0.78, both airfoils experience fully attached flow and nearly flat loss behavior. This strongly aligns with the design level predictions made. At conditions beyond expected operating conditions, the L2FHW displayed resistance to un-reattaching separations at all conditions down to exit Mach 0.2 Reynolds number 23,300. The L3FHW showed un-reattaching separations at only the most extreme condition tested, exit Mach 0.2 and Reynolds number 25,300. (open full item for complete abstract)

Master of Science, Miami University, 2024, Mechanical and Manufacturing Engineering

Wire electrical discharge machining (WEDM) enables production of complex parts with tight tolerances, although maintaining dimensional accuracy in corners and tapers remains challenging due to wire deflection and vibration. This study optimizes WEDM parameters for achieving high-accuracy in machining complex geometrical parts and taper cuts in 6061 Aluminum alloy using Excetek W350G WEDM machine with a copper wire electrode. Parameters including Wire Tension, Pulse On-Time, Pulse Off-Time, Wire Feed Rate, Open Circuit Voltage, and Flashing Pressure were varied using L18 Taguchi Orthogonal Array and response graph method to identify optimal cutting conditions. Results indicated feature-specific optimization is crucial, as different geometrical features (rectangular fins, triangular fins, gears) exhibited varying critical parameters. Key findings highlighted the importance of Wire Tension and Pulse On-Time in maintaining cutting accuracy, although at varying levels for specific features. Response graphs demonstrated effects of major WEDM parameters on corner and profile accuracies, whereas Taguchi analysis provided optimum settings of parameters for each feature and taper cutting. Validation experiments for rectangular fins showed significant improvement in the dimensional error for the fin length and taper angle. These advancements will enhance precision, efficiency, and versatility of WEDM processes in machining complex profiles, and corners, contributing to precision manufacturing.