The aerospace industry relies on nondestructive evaluation (NDE) to ensure aircraft safety and will benefit from methods that allow for early damage detection. Photoluminescence piezospectroscopy (PS) has demonstrated stress and damage sensing of substrates when coupled with alpha-alumina nanoparticles in a polymer matrix applied as a sensor coating. Alpha phase alumina exhibits photoluminescent spectral emission lines (R-lines) that shift due to changes in the stress state of the alumina. The coatings' capability for sensing early subsurface damage suggests the potential for implementing stress sensing paint for integrity monitoring of aircraft structures. To achieve a viable stress sensing coating that can be applied as a paint, materials for optimal sensing and processing need to be tailored for aircraft applications. In addition, advances in optics technology for area measurement and faster data collection are needed. In this work, manufacturing of the sensing paint was achieved by introducing alumina nanoparticles into an aircraft grade topcoat using 3 different processing approaches and the paint with the best dispersion was identified using quantitative luminescence intensity results. To maintain the ease of application through spraying, dispersant was added to the paint. Tensile tests on composite and aluminum substrates resulted in spectral shifts with applied loading that reveal non-uniform and non-recoverable stresses within the paint. Scanning electron microscopy showed microcracks verifying that the sensing paint experienced damage during loading. R1 peaks shift as the paint was heated and cooled, indicating the possibility that the paint is sensitive to temperature changes. Future iterations of the sensing paint will focus on improvements in polymer mechanical properties and homogeneity on application, particle-to-polymer bonding and enhanced adhesion. Area measurement was achieved through the development and calibration of a hyperspectral imaging system using a laser with wider aperture. The long-term goal is to establish a standardized paint-based PS coating and optics technology for structural integrity monitoring of aircraft structures.

Boron carbide (B4C) is one of the most important opaque boride ceramics that has high hardness and Young's modulus that along with low density lead to a significant resistance to ballistic impact and, thus, B4C is broadly used as a protective material. B4C has also high neutron capturing cross section; therefore, it is used as control rods and neutron absorption shielding in nuclear reactors. In this work thermal, electrical and mechanical properties of dense B4C ceramics (99%) sintered using Spark Plasma Sintering (SPS) were investigated. The Young's modulus of B4C measured by three different techniques – IE, RUS, and nanoindentation showed a very good overlap in values, which ranges from 419.2 ± 47.3 GPa for nanoindentation to 458.7 GPa for RUS measurements at room temperature. The mean contact pressure-contact depth plots obtained from load-displacement nanoindentation data indicated pop-in events during loading and an "elbow" event during unloading, both of which are indicative of possible structural changes in B4C structure during nanoindentation. The appearance of "elbow" deviations in load-displacement nanoindentation curves of B4C was detected for the first time. The 4-point bending strength of the B4C ceramics was equal to 585 ± 70 MPa with Weibull parameter of 9.9 and scale parameter equal to 611 MPa. The biaxial strength of B4C was measured to be much lower and equal to 238.6 ± 122 MPa with Weibull parameters of 2.2 and scale parameter equal to 271 MPa. To the best of our knowledge the biaxial strength of B4C was also measured for the first time. In this work it was determined that failure of B4C occurred by fully transgranular fracture, with no intergranular failure present on fracture surface. B4C's fracture toughness Klc = 3 ± 0.19 MPa x m1/2 was measured using SEVNB technique, which is similar to previously reported values.

Ordinary differential equations can describe many dynamic systems. When physics is well understood, the time-dependent responses are easily obtained numerically. The particular numerical method used for integration depends on the application. Unfortunately, when physics is not fully understood, the discrepancies between predictions and observed responses can be large and unacceptable. In this thesis, we show how to directly implement integration of ordinary differential equations through recurrent neural networks using Python. We leveraged modern machine learning frameworks, such as TensorFlow and Keras. Besides offering basic models capabilities (such as multilayer perceptrons and recurrent neural networks) and optimization methods, these frameworks offer powerful automatic differentiation. With that, our approach's main advantage is that one can implement hybrid models combining physics-informed and data-driven kernels, where data-driven kernels are used to reduce the gap between predictions and observations. In order to illustrate our approach, we used two case studies. The first one consisted of performing fatigue crack growth integration through Euler's forward method using a hybrid model combining a data-driven stress intensity range model with a physics-based crack length increment model. The second case study consisted of performing model parameter identification of a dynamic two-degree-of-freedom system through Runge-Kutta integration. Additionally, we performed a numerical experiment for fleet prognosis with hybrid models. The problem consists of predicting fatigue crack length for a fleet of aircraft. The hybrid models are trained using full input observations (far-field loads) and very limited output observations (crack length data for only a portion of the fleet). The results demonstrate that our proposed physics-informed recurrent neural network can model fatigue crack growth even when the observed distribution of crack length does not match the fleet distribution.

Presented within this work is a new method for inertial orbit estimation of an object, either known or unknown, adaptable to a network of low-cost observation satellites. The observation satellites would only require a monocular camera for line of sight measurements. Using the line of sight measurements of each observer, a pair of orthogonal geometric planes that intersect both the observation satellite and the target are created. The intersection of the two planes in the inertial frame defines the new measurement model that is implemented with multiple observation nodes. Total system observability is analyzed and the instantaneous (per node) observability is used to remove "bad" measurements from the system. The measurement model is used in an extended Kalman filter framework and the measurement noise nonlinear transformation is addressed. Three cases are presented; first, the minimum number of required observation nodes to produce accurate results if determined. Then, a smaller number of observation nodes is analyzed to highlight the use of the instantaneous observability and its deleterious effect on the filter performance. Finally, the method is expanded out to multiple observation satellites in a constellation. For all cases, the results show that this method is capable of producing accurate orbit estimation that converges in a short time.

Space missions design requires already tested and trusted control algorithms for spacecraft motion. Rapidly testing control algorithms at a low cost is essential. A novel robotic system that emulates orbital motion in a laboratory environment is presented. The system is composed of a six degree of freedom robotic manipulator fixed on top of an omnidirectional ground vehicle accompanied with onboard computer and sensors. The integrated mobile manipulator is used as a testbed to emulate and realize orbital motion and control algorithms. The kinematic relations of the ground vehicle, robotic manipulator and the coupled kinematics are derived. The system is used to emulate an orbit trajectory. The system is scalable and capable of emulating servicing missions, satellite rendezvous and chaser follower problems.

This research addresses various models of a spring-mass system that uses a spring made of a shape memory alloy (SMA). The system model describes the martensite fractions, which are values that describe an SMA's crystalline phases, via differential equations. The model admits and this thesis contrasts two commonly used but distinct assumptions: a homogeneous case where the martensite fractions are constant throughout the spring's cross section, and a bilinear case where the evolution of the martensite fractions only occurs beyond some critical radius. While previous literature has developed a model of the system dynamics under the homogeneous assumption using the martensite-fractions differential equations, little research has focused on the dynamics when considering the bilinear case, especially using the differential equations. This thesis models the system dynamics under both the homogeneous and bilinear assumptions and determines if the bilinear case is an improvement over the homogeneous case. The research develops a numerical approach of the system dynamics for both martensite-fractions assumptions. For various initial displacements and temperatures, plotting the resulting displacement, velocity, and martensite fractions over time determines the coherence of the assumptions. Not only did the bilinear assumption offer more reasonable plots, but the homogeneous assumption delivered bizarre results for certain temperatures and initial displacements. For future research, a fully nonlinear case can replace the homogeneous and bilinear assumptions. Additionally, future research can utilize other martensite-fractions evolution models, as opposed to differential equations.

With the increasing data handling and power requirements of today's spacecraft, accurately modeling the effects of cabling on spacecraft structural dynamics has become an increasingly important part of the design process. During testing, spacecraft cabling produces a damping effect on the system dynamics; however, current models often overpredict this response in higher frequency modes and produce unrealistic damping values. Previous models incorporated structural and viscous damping terms into Euler-Bernoulli and shear beams; this thesis presents a viscous damping model for Timoshenko beams that can accurately capture the effects of both spacecraft wiring and harnesses during the design phase. Damping in built-up structures shows a weak frequency-dependence; therefore, it is of interest to develop a combination of damping terms and coefficients that provide approximately frequency-independent modal damping. Where previous work included a rotation-based damping term to Euler-Bernoulli beam equations to produce frequency-independent damping, this thesis includes higher-order derivative damping terms to characterize their motion. Because Timoshenko beams account for the effects of both transverse shear and rotary inertia, it is of interest to characterize the damping coefficients using these parameters. Finally, deformed beam shapes were studied to further characterize each damping term as a physical dissipative mechanism.

Carbon fiber composites have become popular in aerospace structures and applications due to their light weight, high strength, and high performance. Hybrid carbon fiber reinforced polymer (HCFRP) composites with alumina nanoparticles reinforcement display improved material properties such as fracture toughness, resistance to crack propagation and improved fatigue life. However, homogeneous dispersion of nanoscale materials in the matrix is important for even distribution of the improved properties. Implementing silane coupling agents (SCAs) improves dispersion by acting as a bridge between organic and inorganic materials, which increases interfacial strength and decreases sedimentation by bonding the particulate filler to the fiber reinforcement. This research is aimed at quantifying the improvement in dispersion of nanoparticles and elucidating the effects on the mechanical property of HCFRP samples through the novel use of photoluminescent characteristic peaks emitted by the alumina reinforcement particles. Photo-luminescene emission from secondary reinforcement particles of alumina embedded within the hybrid carbon fiber composites is leveraged to reveal microstructural effects of functionalization and particle weight fraction as it relates to overall composite mechanics. 6, 9 and 12 weight percentage of alumina particle loading with Reactive Silane Coupling Agents, Non-reactive Silane Coupling Agent surface treatments and untreated condition are investigated in this research. Uniaxial tensile tests were conducted with measurements using piezospectroscopy (PS) and concurrent digital image correlation (DIC) to quantify the mechanical property and load distribution between the carbon fiber/epoxy and the reinforcing nanoparticles. The piezospectroscopic data were collected in an in-situ configuration using a portable piezospectroscopy system while the sample was under tensile load. Photoluminescence results show the dispersion and sedimentation behavior of the nanoparticles in the material for different surface treatment and weight percentage of the alumina nanoparticles. The piezospectroscopic maps capture and track the residual stress and its change under applied load. The results reveal the effect of varying particle loading on composite mechanical properties and how this changes with different functionalization conditions. The role of the particles in load transfer in the hybrid composite is further investigated and compared with theory. This work extends the capability of spectroscopy as an effective non-invasive method to study, at the microstructural level, the material and manufacturing effects on the development of advanced composites for applications in aerospace structures and beyond.

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.