Thesis (M.S.)--University of Wisconsin--Madison, 1966. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (M.S. in Systems Technology (Space Systems Operations))--Naval Postgraduate School, June 1990. / Thesis Advisor(s): Boger, Dan C. ; Loomis, Herschel H. "June 1990." Description based on signature page as viewed on October 21, 2009. DTIC Identifier(s): Maneuvering satellites, logistics support, space maintenance, OMV (Orbital Maneuvering Vehicles), space vehicles, modular space construction, cost estimates, polar orbit trajectories, space logistics support, theses. Author(s) subject terms: Orbital Maneuvering Vehicle, satellite servicing, polar satellites, expendible launch vehicles, low earth orbit. Includes bibliographical references (p. 106-109). Also available online.

Thesis (Ph. D., Chemical Engineering)--University of Idaho, December 2007. / Major professor: Aaron M. Thomas. Includes bibliographical references (leaves 214-217). Also available online (PDF file) by subscription or by purchasing the individual file.

Dissertation (Ph.D. in Astronautical Engineering)--Naval Postgraduate School, March 2010. / Dissertation supervisor: Romano, Marcello. "March 2010." Description based on title screen as viewed on April 30, 2010. Author(s) subject terms: spacecraft proximity operations, Inverse Dynamics in the Virtual Domain, rapid-trajectory generation, spacecraft rendezvous, spacecraft docking, autonomous assembly, Pontryagin, Minimum Principle, GPOPS, optimal reorientation, optimal rendezvous, quaternion, polynomial. Includes bibliographical references (p. 193-197). Also available in print.

Dust problems raise significant concerns in planetary surface exploration. The unusual behavior of the dust particles that surround the vehicle after engine cutoff has the potential to have more of an influence on surface systems than the high velocity lunar rocket plume ejecta in the landing process. A prevailing hypothesis attributes the levitation and transport of dust particles on the surface of airless bodies to the electrostatic effects and electric field. However, there is no accurate model considering the inter-particle electrostatic interactions, especially when the particles are charged by plume. This dissertation aims to understand the behavior of charged lunar regolith with a discrete element method (DEM) approach focusing on the inter-particle interactions and contact charge transfer. To accomplish this, the grain dynamics is coupled with mechanical and electrical particle interactions, and both short-range and long-range interactions between particles are incorporated. A tribo-charging model based on instantaneous collisions is adopted and validated by simulation and experimental data. Sensitivity analysis is conducted to quantify the effects of initial charge, tribo-charging, and E-field on transport of lunar dust. DEM simulations are then performed for a near realistic lunar environment that show the differences of position and velocity distributions between charged particles and uncharged particles. The results indicate that the charged dust particles have higher dispersion and wider distribution of velocity due to the electrostatic effects. This provides a possible explanation for the phenomena of the approximately 30 s dust lofting following Apollo Lunar Module landing. It is shown that tribo-charging has a more considerable effect on the dynamics of charged particles with a large amount of charge, while the change of E-field does not significantly affect the results. Furthermore, superquadrics and multi-sphere approximations are introduced as two approaches to aspherical geometry to accomplish high-fidelity simulation of lunar dust in the future.

Thin-ply composite laminates capable of enduring high strains are currently under investigation for compliant deployable spacecraft structures. Deployable structures such as booms fabricated from these materials can be flattened and coiled to high curvatures, achieving a compact configuration for stowage. Once in orbit, they are released with minimal actuation for deployment, allowing the operational geometry to be recovered. Previous studies have shown that the viscoelastic properties of the composite epoxy matrix can negatively impact final shape accuracy due to stress relaxation during stowage. In addition, since the strain energy stored is relied upon for deployment, considerable relaxation can potentially result in deployment stall. Stress relaxation in composites and the aforementioned effects it can have on deployment have not been analyzed sufficiently for space applications. The objective of this thesis is to investigate the moment relaxation and curvature recovery behavior of thin-ply composite laminates through a combination of analytical, numerical, and experimental approaches. The viscoelastic Kirchhoff plate model that serves as the theoretical basis of the analyses is first presented. An analytical solution for the recovery of a composite plate after stowage is derived. The numerical integration of the viscoelastic plate constitutive equations and its implementation as a user-defined subroutine in finite element programs is then described. The subroutine allows relaxation of 3D thin-shell structures to be modeled, and is applied to simulate stowage and recovery of a thin-ply composite currently of interest for solar sailing applications. The subroutine is then compared with results obtained from experiments for a thin-ply composite for bending relaxation and curvature creep recovery after being unloaded.

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.