Extreme operating temperatures within the turbine section of jet engines require sophisticated methods of cooling and material protection. Thermal barrier coatings (TBCs) achieve this through a ceramic coating applied to a substrate material (nickel-based superalloy). Electron-beam physical vapor deposition (EB-PVD) is the industry standard coating used on jet engines. By tailoring the microstructure of an emerging deposition method, Plasma-spray physical vapor deposition (PS-PVD), similar microstructures to that of EB-PVD coatings can be fabricated, allowing the benefits of strain tolerance to be obtained while improving coating deposition times. This work investigates the strain through depth of uncycled and cycled samples using these coating techniques with synchrotron X-ray diffraction (XRD). In the TGO, room temperature XRD measurements indicated samples of both deposition methods showed similar in-plane compressive stresses after 300 and 600 thermal cycles. In-situ XRD measurements indicated similar high-temperature in-plane and out-of-plane stress in the TGO and no spallation after 600 thermal cycles for both coatings. Tensile in-plane residual stresses were found in the YSZ uncycled PS-PVD samples, similar to APS coatings. PS-PVD samples showed in most cases, higher compressive residual in-plane stress at the YSZ/TGO interface. These results provide valuable insight for optimizing the PS-PVD processing parameters to obtain strain compliance similar to that of EB-PVD. Additionally, external cooling methods used for thermal management in jet engine turbines were investigated. In this work, an additively manufactured lattice structure providing transpiration cooling holes is designed and residual strains are measured within an AM transpiration cooling sample using XRD. Strains within the lattice structure were found to have greater variation than that of the AM solid wall. These results provide valuable insight into the viability of implementing an AM lattice structure in turbine blades for the use of transpiration cooling.

Using quadcopters for analysis of an environment has been an intriguing subject of study recently. The purpose of this work is to develop a fully autonomous UAV platform for Railroad inspection The dynamics of the quadrotor is derived using Euler's and Newton's laws and then linearized around the hover position. A PID controller is designed to control the states of the quadrotor in a manner to effectively follow a vision-based path, using the down facing camera on a Parrot Mambo quadrotor. Using computer vision the distance from the position of the quadrotor to the position of the center of the path was found. Using the yaw controller to minimize this distance was found to be an adequate method of vision-based path following, by keeping the area of interest in the field of view of the camera. The downfacing camera is also simultaneously observing the path to detect defects using machine learning. This technique was able to detect simulated defects on the path with around 90% accuracy.

As populations and the demand for higher crop yields grow, so to does the need for efficient agricultural wheeled mobile robots. To achieve precise navigation through a field it is desirable that the control system is designed based on an accurate dynamic model. In this paper a control affine model for a custom designed skid-steer differential drive wheeled mobile robot is found. The Terramechanic wheel terrain interaction is adopted and modified to consider wheels with a torus geometry. Varying slip ratios and slip angles are considered in the terrain reaction forces, which is curve-fitted using a nonlinear least squares approach such that the achieved model is control affine. The parameters in the proposed model is identified through an extended Kalman filter so that the state variables in the model are matched. Both simulation and experiments in a commercial farm validated the proposed model and the identification approach.

The long duration airborne feature of airships makes them an attractive solution for many military and civil applications such as long-endurance surveillance, reconnaissance, environment monitoring, communication utilities, and energy harvesting. To achieve a minimum energy periodic motion in the air, an optimal trajectory problem is solved using basic direct collocation methods. In the direct approach, the optimal control problem is converted into a nonlinear programming (NLP). Pseudo-inverse and several discretization methods such as Trapezoidal and Hermite-Simpson are used to obtain a numerical approximated solution by discretizing the states and controls into a set of equal nodes. These nodes are approximated by a cubic polynomial function which makes it easier for the optimization to converge while ensuring the problem constraints and the equations of motion are satisfied at the collocation points for a defined trajectory. In this study, direct collocation method provides the ability to obtain an approximation solution of the minimum energy expenditure of a very complex dynamic problem using Matlab fmincon optimization algorithm without using Himiltonian function with Lagrange multipliers. The minimal energy trajectory of the airship is discussed and results are presented.

This thesis focuses on the development a streamlined process used to create novel meso-scale pattern used to induce negative Poisson's ratio (NPR) behavior at the bulk scale. This process includes, the development, optimization, and implementation of a candidate pattern. Currently, the majority of NPR structures are too porous to be utilized in conventional applications. For others, manufacturing methods have yet to realize the meso-scale pattern. Consequently, new NPR meta-materials must be developed in order to confer transformative thermomechanical responses to structures where transverse expansion is more desirable than contraction. For example, materials at high temperature. Additionally, patterns that take into account manufacturing limitations, while maintaining the properties characteristically attached to negative Poisson's Ratio materials, are ideal in order to utilize the potential of NPR structures. A novel NPR pattern is developed, numerically analyzed, and optimized via design of experiments. The parameters of the meso-structure are varied, and the bulk response is studied using finite element analysis (FEA). The candidate material for the study is Medium-Density Fiberboard (MDF). This material is relevant to a variety of applications where multiaxial stresses, particularly compressive, lead to mechanical fatigue. Samples are fabricated through a laser cutting process, and a comparison is drawn through the use of experimental means, including traditional tensile loading tests and digital image correlation (DIC). Various attributes of the elasto-plasticity responses of the bulk structure are used as objectives to guide the optimization process.

As the use of Unmanned Aerial Systems (UAS) becomes more common in both civilian/commercial and military applications, so too has the risk of injury to individuals and third parties on the ground. The purpose of this research is to further enhance methods currently in use for performing flight path risk assessment for UAS, as well as improve upon an existing software tool: Quantitative Approach and Departure Risk Assessment (QUADRA). The primary focus is upon the incorporation of building information to determine the protection offered to sheltered populations, reevaluate the probability of fatality models used in aircraft failures to more accurately determine the risk for smaller UAS systems, and to provide a metric for determining the number of individuals that are adversely affected by the noise of the autonomous system as it performs its mission. / Master of Science / Unmanned Aerial Vehicles are aircraft that are operated without a pilot onboard. More conventionally known as drones, these aircraft can be at increased risk to individuals on the ground as there is no pilot in the aircraft to course correct should the aircraft fail. Due to the potential for drones to fail and thus injure people on the ground, a method for determining the number of people injured or killed by an aircraft for its mission has been developed. These methods identify areas on the ground where the aircraft may land, as well as the potential number of fatalities for a given mission. To minimize the risk to people on the ground, the flight path of the drone is changed until a lower risk flight path is found. Similarly, the sound produced by these drones is used to determine the number of people who may hear the aircraft as it is flying overhead, as well as the number of people who may be annoyed or disturbed by this noise.

Recently, novel experimental evidence of continuous rotating detonations for gaseous H2/O2 propellants with a rotating detonation rocket engine (RDRE) was attained on the 3-inch Air Force Research Laboratory (AFRL) Distribution A RDRE, with the fuel and oxidizer injectors modified for H2/O2 gas propellants. Evident in previous experiments, detonation instabilities arising from upstream deflagration, from recirculation zones, and from insufficient gas mixing challenged resolution of detonation wave behavior from back-end imaging with the available optical equipment. Images were often over-illuminated from both the high amount of deflagration in the plume and the higher density of detonation waves in the annulus coupled with the small detonation cell size for H2/O2 gas propellants. Additionally, conventional optical systems attenuate the ultraviolet (UV) emission range (~308-320 nm wavelength) from the primary combustion species. To overcome these challenges are two methodologies that still utilize optical back-end imaging: (1) CH* chemiluminescence with fuel doping, and (2) OH* chemiluminescence. The first methodology utilizes doping CH4 into the H2/O2 gas mixture at a relatively small concentration of up to 5% by total mass flow rate to leverage CH* chemiluminescence at 409 ± 32 nm wavelength. The second methodology utilizes the combination of an OH* bypass filter for 308–320 nm wavelength to filter other emissions and an intensifier to amplify the detonation wave OH* emission. As of the present research, the first methodology was investigated across a regime of operating conditions, with planned future testing outlined to facilitate comparable data acquisition utilizing the second methodology.

Optimal trajectory design has been extensively studied across multiple disciplines adopting different techniques for implementation and execution. It has been utilized in past space trajectory missions to either optimize the amount of fuel spent or minimize the time of flight to meet mission requirements. Coupled Radial Basis Functions (CRBFs) are a new way to solve these optimal control problems, and this thesis applies CRBFs to spacecraft trajectory optimization design problems. CRBFs are real-valued radial basis functions (RBFs) that utilize a conical spline while also not being affected by the value of the shape parameter. The CRBF approach is applied to nonlinear optimal control problems. We adopt the indirect formulation so that the necessary and boundary conditions are derived from the system dynamical equations. As a result, a set of nonlinear algebraic equations (NAEs) is generated. The NAEs are then solved using a standard solver in MATLAB and the results are produced. CRBFs do not rely heavily on initial extensive analysis of the problem, which makes it very intuitive to use. The states, control, and co-states are defined as the equations to be solved and approximated using CRBFs. The results show that CRBFs can be applied to space trajectory optimization problems to produce accurate results across state and costate variables on uniform user defined nodes across the simulation time.

To model and examine the thermal fluid phenomena involved in high-pressure, multi-nozzle spray cooling, a testbed is developed which includes a heating subsystem and an accumulator to pressurize common rail based piezoelectric injectors. Compared to conventional platforms, the implemented testbed allows for an abundance of layout arrangements and settings that provide a greater range of functionality. The volumetric flow rate of the testbed is modeled by a recurrent neural network trained from time-sequential obtained through experiments. The fidelity of the model, as well as the testbed's hardware, software, functionalities, and shortcomings are discussed.

In the past few decades, Kessler syndrome (named after Donald J. Kessler) has become a point of concern in the field of Space Situational Awareness and the future of space missions. It refers to a scenario, where space debris in Earth's orbits collides and creates an exponential increase in space debris numbers leading to more collisions and more debris. In order to handle the resulting challenges like conjunction analysis, tracking, and probability of collisions, the State Transition Matrix (STM) and Tensors (STTs) of the orbit problem play a significant role. In addition, STM and STTs are ubiquitous in spaceflight dynamics, guidance, navigation, and control applications. Several methods exist in the literature for computing the STM and the STTs of the orbit problem; however, all these methods are either restricted by a simplified gravity model, computational accuracy or computational efficiency. In this dissertation, an adaptive Analytic Continuation is studied as a procedure for computing the STM and STTs of the perturbed Two-body problem. Analytic Continuation is a Taylor series based semi-analytic integration method that utilizes recursions of high-order time derivatives and the Leibniz rule to produce a solution with arbitrary accuracy at a fraction of the computational cost of finite difference methods. In this work, the method is used to compute the STM and the second order STT for the perturbed two-body problem. An adaptation technique is developed for keeping a balance between the number of higher order time derivatives and the time-step to achieve prescribed tolerances. Analytic Continuation is also adopted in a high-fidelity estimation framework (AC-EKF) to provide accurate orbit estimation results for a space-based space surveillance network of observers. Test cases on LEO, MEO, GTO and HEO show machine precision accuracy in the symplectic nature of the gravity perturbed STM and STT irrespective of the number of orbital revolutions. Gravity and atmospheric drag perturbed STM shows at least 3 times more accurate results when compared to finite difference methods in the initial error propagation of the trajectories in a span of 10 orbit periods. Furthermore, by incorporating second order STT, the error propagation results are improved by 2 - 4 orders of magnitude. Finally, results from AC-EKF show the utility of the method to accurately predict the error covariance in the absence of sensor coverage. As future work, Analytic Continuation will be expanded to compute arbitrarily high-order STTs with applications in orbit prediction and trajectory design.