Carbon nanotubes (CNTs) have emerged as compelling materials for enhancing both electrical and mechanical properties of aerospace structures. Buckypaper (BP), a porous membrane consisting of a highly cross-linked network of CNTs, can be effectively integrated with carbon fiber reinforced polymer (CFRP) composites to simultaneously enhance their electromagnetic interference (EMI) shielding effectiveness (SE) and mechanical properties. In existing literature, CNT based nanocomposites are shown to improve the flexural strength and stiffness of CFRP laminates. However, a limited amount of research has been reported in predicting the EMI SE of hybrid BP embedded CFRP composites. To characterize the EMI shielding response of hybrid BP/CFRP laminates, a novel modeling approach based on equivalent electrical circuits is employed to estimate the electrical conductivity of unidirectional CFRP plies. This approach uses Monte Carlo simulations and accounts for the effects of quantum tunneling at the fiber-fiber contact region. This study specifically examines a signal frequency range of 50 MHz to 12 GHz, corresponding to the very high to X band spectrum. The results indicate that at a frequency of 12 GHz, the longitudinal conductivity decreases to around ~3,300 S/m from an initial DC value of 40,000 S/m, while the transverse conductivity concurrently increases from negligible to approximately ~12.67 S/m. These results are then integrated into Ansys High Frequency Structure Simulator (HFSS) to predict EMI SE by simulating the propagation of electromagnetic waves through a semi-infinite composite shield representative volume element. The numerical simulations illustrate that incorporating BP allows for significant ii improvements in SE of hybrid BP/CFRP composites. At 12 GHz signal frequency, for example, the incorporation of a single BP interleave enhances the SE of a [90,0] laminate by up to ~64%, while the incorporation of two BP interleaves in a [90,0,+45,-45,0,90]s balanced symmetric laminate enhances its SE by ~20% . This enhancement is due to the high conductivity of BP at high frequencies. Additionally, to evaluate the flexural property enhancements due to BP, experimental three-point bend tests were conducted on different configurations of hybrid BP/CFRP laminates, and their strength and stiffness were compared with the non-BP samples. Micrographs of failed samples are acquired using an optical microscope, which provides insights into their underlying damage mechanisms. Fractography analysis confirms the role of BP in preventing through-thickness crack propagation, attributed to the excellent crack retardation properties of CNTs.

The propagation of waves in solids, especially when characterized by dispersion, remains a topic of profound interest in the field of signal processing. Dispersion represents a phenomenon where wave speed becomes a function of frequency and results in multiple oscillatory modes. Such signals find application in structural healthmonitoring for identifying potential damage sensitive features in complex materials. Consequently, it becomes important to find matched time-frequency representations for characterizing the properties of the multiple frequency-dependent modes of propagation in dispersive material. Various time-frequency representations have been used for dispersive signal analysis. However, some of them suffered from poor timefrequency localization or were designed to match only specific dispersion modes with known characteristics, or could not reconstruct individual dispersive modes. This thesis proposes a new time-frequency representation, the nonlinear synchrosqueezing transform (NSST) that is designed to offer high localization to signals with nonlinear time-frequency group delay signatures. The NSST follows the technique used by reassignment and synchrosqueezing methods to reassign time-frequency points of the short-time Fourier transform and wavelet transform to specific localized regions in the time-frequency plane. As the NSST is designed to match signals with third order polynomial phase functions in the frequency domain, we derive matched group delay estimators for the time-frequency point reassignment. This leads to a highly localized representation for nonlinear time-frequency characteristics that also allow for the reconstruction of individual dispersive modes from multicomponent signals. For the reconstruction process, we propose a novel unsupervised learning approach that does not require prior information on the variation or number of modes in the signal. We also propose a Bayesian group delay mode merging approach for reconstructing modes that overlap in time and frequency. In addition to using simulated signals, we demonstrate the performance of the new NSST, together with mode extraction, using real experimental data of ultrasonic guided waves propagating through a composite plate.

It is well–established that physical phenomena occurring at the macroscale are the result of underlying molecular mechanisms that occur at the nanoscale. Understanding these mechanisms at the molecular level allows the development of semicrystalline polymers with tailored properties for different applications. Molecular Dynamics (MD) simulations offer significant insight into these mechanisms and their impact on various physical and mechanical properties. However, the temporostpatial limitations of all–atomistic (AA) MD simulations impede the investigation of phenomena with higher time– and length–scale. Coarse–grained (CG) MD simulations address the shortcomings of AAMD simulations by grouping atoms based on their chemical, structural, etc., aspects into larger particles, beads, and reducing the degrees offreedom of the atomistic system, allowing achievement of higher time– and length–scales. Among the approaches for generating CG models, the hybrid approach is capable of capturing the underlying mechanisms at the molecular level while replicating phenomena at temporospatial scales attainable by the CG model. In this dissertation, a novel hybrid method is developed for the systematic coarse–graining of semicrystalline polymers that uniquely blends the potential functions of both phases. The obtained blended potential not only faithfully reproduces the structural distributions of multiple phases simultaneously but also allows control over the dynamics of the obtained CG models employing a tunable parameter. Given that accelerated dynamics of the CG models hinder the investigation of phenomena in the crystal phase, such as α–α-relaxation, by utilizing the developed method, this phenomenon was successfully modeled for a semicrystalline polyethylene (PE) system with obtained values for the diffusion constant at room temperature and the activation energy in close agreement with experimental results. In a subsequent study, a family of potentials was developed for a sample semicrystalline polyethylene (PE) to investigate the impact of different potential functions on some physical properties, such as crystal diffusion and glass transition temperature, and their correlation with some mechanical properties obtained from uniaxial deformation.

In this research, the chemical and mineralogical compositions, physical and mechanical properties, and failure mechanisms of two ordinary chondrite (OCs) meteorites Aba Panu (L3) and Viñales (L6), and the iron meteorite called Gibeon (IVA) were studied. OCs are dominated by anhydrous silicates with lesser amounts of sulfides and native Fe-Ni metals, while Gibeon is primarily composed of Fe-Ni metals with scattered inclusions of graphite and troilite. The OCs were investigated to understand their response to compressive loading, using a three-dimensional (3-D) Digital Image Correlation (DIC) technique to measure full-field deformation and strain during compression. The DIC data were also used to identify the effects of mineralogical and structural heterogeneity on crack formation and growth. Even though Aba Panu and Viñales are mineralogically similar and are both classified as L ordinary chondrites, they exhibit differences in compressive strengths due to variations in chemical compositions, microstructure, and the presence of cracks and shock veins. DIC data of Aba Panu and Viñales show a brittle failure mechanism, consistent with the crack formation and growth from pre-existing microcracks and porosity. In contrast, the Fe-Ni phases of the Gibeon meteorite deform plastically without rupture during compression, whereas during tension, plastic deformations followed by necking lead to final failure. The Gibeon DIC results showed strain concentration in the tensile gauge region along the sample edge, resulting in the initiation of new damage surfaces that propagated perpendicular to the loading direction. Finally, an in-situ low-temperature testing method of iron meteorites was developed to study the response of their unique microstructure and failure mechanism.

Advanced fibrous composite materials exhibit outstanding thermomechanical performance under extreme environments, which make them ideal for structural components that are used in a wide range of aerospace, nuclear, and defense applications. The integrity and residual useful life of these components, however, are strongly influenced by their inherent material flaws and defects resulting from the complex fabrication processes. These defects exist across multiple length scales and govern several scale-dependent inelastic deformation mechanisms of each of the constituents as well as their composite damage anisotropy. Tailoring structural components for optimal performance requires addressing the knowledge gap regarding the microstructural material morphology that governs the structural scale damage and failure response. Therefore, there is a need for a high-fidelity multiscale modeling framework and scale-specific in-situ experimental characterization that can capture complex inelastic mechanisms, including damage initiation and propagation across multiple length scales. This dissertation presents a novel multiscale computational framework that accounts for experimental information pertinent to microstructure morphology and architectural variabilities to investigate the response of ceramic matrix composites (CMCs) with manufacturing-induced defects. First, a three-dimensional orthotropic viscoplasticity creep formulation is developed to capture the complex temperature- and time-dependent constituent load transfer mechanisms in different CMC material systems. The framework also accounts for a reformulated fracture mechanics-informed matrix damage model and the Curtin progressive fiber damage model to capture the complex scale-dependent damage and failure mechanisms through crack kinetics and porosity growth. Next, in-situ experiments using digital image correlation (DIC) are performed to capture the damage and failure mechanisms in CMCs and to validate the high-fidelity modeling results. The dissertation also presents an exhaustive experimental investigation into the effects of temperature and manufacturing-induced defects on toughened epoxy adhesives and hybrid composite-metallic bonded joints. Nondestructive evaluation techniques are utilized to characterize the inherent defects morphology of the bulk adhesives and bonded interface. This is followed by quasi-static tensile tests conducted at extreme hot and cold temperature conditions. The damage mechanisms and failure modes are investigated using in-situ DIC and a high-resolution camera. The information from the morphology characterization studies is used to reconstruct high-fidelity geometries of the test specimens for finite element analysis.

Intelligent engineering designs require an accurate understanding of material behavior, since any uncertainties or gaps in knowledge must be counterbalanced with heightened factors of safety, leading to overdesign. Therefore, building better structures and pushing the performance of new components requires an improved understanding of the thermomechanical response of advanced materials under service conditions. This dissertation provides fundamental investigations of several advanced materials: thermoset polymers, a common matrix material for fiber-reinforced composites and nanocomposites; aluminum alloy 7075-T6 (AA7075-T6), a high-performance aerospace material; and ceramic matrix composites (CMCs), an advanced composite for extreme-temperature applications. To understand matrix interactions with various interfaces and nanoinclusions at their fundamental scale, the properties of thermoset polymers are studied at the atomistic scale. An improved proximity-based molecular dynamics (MD) technique for modeling the crosslinking of thermoset polymers is carefully established, enabling realistic curing simulations through its ability to dynamically and probabilistically perform complex topology transformations. The proximity-based MD curing methodology is then used to explore damage initiation and the local anisotropic evolution of mechanical properties in thermoset polymers under uniaxial tension with an emphasis on changes in stiffness through a series of tensile loading, unloading, and reloading experiments. Aluminum alloys in aerospace applications often require a fatigue life of over 109 cycles, which is well over the number of cycles that can be practically tested using conventional fatigue testing equipment. In order to study these high-life regimes, a detailed ultrasonic cycle fatigue study is presented for AA7075-T6 under fully reversed tension-compression loading. The geometric sensitivity, frequency effects, size effects, surface roughness effects, and the corresponding failure mechanisms for ultrasonic fatigue across different fatigue regimes are investigated. Finally, because CMCs are utilized in extreme environments, oxidation plays an important role in their degradation. A multiphysics modeling methodology is thus developed to address the complex coupling between oxidation, mechanical stress, and oxygen diffusion in heterogeneous carbon fiber-reinforced CMC microstructures.

Uncertainty quantification is critical for engineering design and analysis. Determining appropriate ways of dealing with uncertainties has been a constant challenge in engineering. Statistical methods provide a powerful aid to describe and understand uncertainties. This work focuses on applying Bayesian methods and machine learning in uncertainty quantification and prognostics among all the statistical methods. This study focuses on the mechanical properties of materials, both static and fatigue, the main engineering field on which this study focuses. This work can be summarized in the following items: First, maintaining the safety of vintage pipelines requires accurately estimating the strength. The objective is to predict the reliability-based strength using nondestructive multimodality surface information. Bayesian model averaging (BMA) is implemented for fusing multimodality non-destructive testing results for gas pipeline strength estimation. Several incremental improvements are proposed in the algorithm implementation. Second, the objective is to develop a statistical uncertainty quantification method for fatigue stress-life (S-N) curves with sparse data.Hierarchical Bayesian data augmentation (HBDA) is proposed to integrate hierarchical Bayesian modeling (HBM) and Bayesian data augmentation (BDA) to deal with sparse data problems for fatigue S-N curves. The third objective is to develop a physics-guided machine learning model to overcome limitations in parametric regression models and classical machine learning models for fatigue data analysis. A Probabilistic Physics-guided Neural Network (PPgNN) is proposed for probabilistic fatigue S-N curve estimation. This model is further developed for missing data and arbitrary output distribution problems. Fourth, multi-fidelity modeling combines the advantages of low- and high-fidelity models to achieve a required accuracy at a reasonable computation cost. The fourth objective is to develop a neural network approach for multi-fidelity modeling by learning the correlation between low- and high-fidelity models. Finally, conclusions are drawn, and future work is outlined based on the current study.

Structural/system health monitoring (SHM) and prognostic health management (PHM) are vital techniques to ensure engineering system reliability and safety during the service. As multi-functionality and enhanced performance are in demand, modern engineering systems including aerospace, mechanical, and civil applications have become more complex. The constituent and architectural complexity, and multisource sensing sources in modern engineering systems may limit the monitoring capabilities of conventional approaches and require more advanced SHM/PHM techniques. Therefore, a hybrid methodology that incorporates information fusion, nondestructive evaluation (NDE), machine learning (ML), and statistical analysis is needed for more effective damage diagnosis/prognosis and system safety management.This dissertation presents an automated aviation health management technique to enable proactive safety management for both aircraft and national airspace system (NAS). A real-time, data-driven aircraft safety monitoring technique using ML models and statistical models is developed to enable an early-stage upset detection capability, which can improve pilot’s situational awareness and provide a sufficient safety margin. The detection accuracy and computational efficiency of the developed monitoring techniques is validated using commercial unlabeled flight data recorder (FDR) and reported accident FDR dataset. A stochastic post-upset prediction framework is developed using a high-fidelity flight dynamics model to predict the post-impacts in both aircraft and air traffic system. Stall upset scenarios that are most likely occurred during loss of control in-flight (LOC-I) operation are investigated, and stochastic flight envelopes and risk region are predicted to quantify their severities. In addition, a robust, automatic damage diagnosis technique using ultrasonic Lamb waves and ML models is developed to effectively detect and classify fatigue damage modes in composite structures. The dispersion and propagation characteristics of the Lamb waves in a composite plate are investigated. A deep autoencoder-based diagnosis technique is proposed to detect fatigue damage using anomaly detection approach and automatically extract damage sensitive features from the waves. The patterns in the features are then further analyzed using outlier detection approach to classify the fatigue damage modes. The developed diagnosis technique is validated through an in-situ fatigue tests with periodic active sensing. The developed techniques in this research are expected to be integrated with the existing safety strategies to enhance decision making process for improving engineering system safety without affecting the system’s functions.

This paper focuses on the sample preparation and material characterization of a carbon fiber-reinforced silicon carbonitride (C/SiNC) ceramic matrix composite (CMC) system. C/SiNC CMC systems have desirable mechanical and thermal properties which makes them suitable for a wide variety of applications ranging from aerospace to power generation. CMCs are highly susceptible to manufacturing-induced defects, and the effect of these defects on the microscale damage behavior of the microstructure of these CMCs has not been researched. In order to perform the material characterization study, samples of the C/SiNC CMC system had to be prepared through a meticulous polishing process. After the samples were prepared, micrographs of the intratow region of the samples were captured using a confocal microscope. Feature extraction were subsequently performed on the micrographs that were captured. Different image processing techniques were applied to the captured micrographs to quantify the features that were identified.