Solar photovoltaic (PV) generation has seen significant growth in 2021, with an increase of around 22% and exceeding 1000 TWh. However, this has also led to reliability and durability issues, particularly potential induced degradation (PID), which can reduce module output by up to 30%. This study uses cell- and module-level analysis to investigate the impact of superstrate, encapsulant, and substrate on PID.The influence of different substrates and encapsulants is studied using one-cell modules, showing that substrates with poor water-blocking properties can worsen PID, and encapsulants with lower volumetric resistance can conduct easily under damp conditions, enabling PID mechanisms (results show maximum degradation of 9%). Applying an anti-soiling coating on the front glass (superstrate) reduces PID by nearly 53%. Typical superstrates have sodium which accelerates the PID process, and therefore, using such coatings can lessen the PID problem. At the module level, the study examines the influence of weakened interface adhesion strengths in traditional Glass-Backsheet (GB) and emerging Glass-Glass (GG) (primarily bifacial modules) constructions. The findings show nearly 64% more power degradation in GG modules than in GB. Moreover, the current methods for detecting PID use new modules, which can give inaccurate information instead of DH-stressed modules for PID testing, as done in this work. A comprehensive PID susceptibility analysis for multiple fresh bifacial constructions shows significant degradation from 20 to 50% in various constructions. The presence of glass as the substrate exacerbates the PID problem due to more ionic activity available from the two glass sides. Recovery experiments are also conducted to understand the extent of the PID issue. Overall, this study identifies, studies, and explains the impact of superstrate, substrate, and encapsulant on the underlying PID mechanisms. Various pre- and post-stress characterization tests, including light and dark current-voltage (I-V) tests, electroluminescence (EL) imaging, infrared (IR) imaging, and UV fluorescence (UVF) imaging, are used to evaluate the findings. This study is significant as it provides insights into the PID issues in solar PV systems, which can help improve their performance and reliability.

Manufacturing production is limited by three main factors, cost, both overall and on a per unit basis, final product quality, and process repeatability or frequency. Even producing small objects through the casting of epoxy resin, a liquid substance capable of hardening when in contact with a catalyst material presents these same issues. There are three distinct areas of epoxy resin casting influenced by each of these manufacturing factors, the material used to create molds, the air process applied to minimize defects, and resin demold time. This investigation was designed to determine the impact the three factors of manufacturing production have on the casting epoxy resin. Each category had numerous tests conducted to determine the best combination of production in terms of low cost, high quality, and high levels of repeatability. Ultimately, the best combination was the use of a platinum silicone called Mold Star 15, a pressure chamber, and an epoxy resin with a 12-hour cure time, called Amazing Resin. The final cost to create 100 products is $410.85. However, it should be noted for the highest quality dice, the utilization of a pressure chamber is required while the mold materials are interchangeable.

The standard for hybrid fuel grains is Hydroxyl-terminated polybutadiene (HTPB). With the advances in additive manufacturing, the promise of 3D printed fuel grains has become a possibility. Yet, 3D printed grains do not have as good of a regression rate as the casted HTPB grains. However, with 3D printing, the core of these grains can be printed to maximize surface area in contact with the oxidizer. The goal of this research is to print hybrid rocket fuel grains with various core geometries and test them on a small-scale hybrid test stand. While the hot fires are still under testing at the time of this abstract, the manufacturing posed an interesting outcome, being more time intensive than expected, contradicting the initial hypothesis of faster manufacturing. Future endeavors will continue research into the cores of the 3D printed grains, possible multi-material made grains and creating core structures for HTPB grains from 3D printed materials.

With the maturity of advanced composites as feasible structural materials for various applications there is a critical need to solve the challenge of designing these material systems for optimal performance. However, determining superior design methods requires a deep understanding of the material-structure properties at various length scales. Due to the length-scale dependent behavior of advanced composites, multiscale modeling techniques may be used to describe the dominant mechanisms of damage and failure in these material systems. With polymer matrix fiber composites and nanocomposites it becomes essential to include even the atomic length scale, where the resin-hardener-nanofiller molecules interact, in the multiscale modeling framework. Additionally, sources of variability are also critical to be included in these models due to the important role of uncertainty in advance composite behavior. Such a methodology should be able to describe length scale dependent mechanisms in a computationally efficient manner for the analysis of practical composite structures.

In the research presented in this dissertation, a comprehensive nano to macro multiscale framework is developed for the mechanical and multifunctional analysis of advanced composite materials and structures. An atomistically informed statistical multiscale model is developed for linear problems, to estimate and scale elastic properties of carbon fiber reinforced polymer composites (CFRPs) and carbon nanotube (CNT) enhanced CFRPs using information from molecular dynamics simulation of the resin-hardener-nanofiller nanoscale system. For modeling inelastic processes, an atomistically informed coupled damage-plasticity model is developed using the framework of continuum damage mechanics, where fundamental nanoscale covalent bond disassociation information is scaled up as a continuum scale damage identifying parameter. This damage model is coupled with a nanocomposite microstructure generation algorithm to study the sub-microscale damage mechanisms in CNT/CFRP microstructures. It is further integrated in a generalized method of cells (GMC) micromechanics model to create a low-fidelity computationally efficient nonlinear multiscale method with imperfect interfaces between the fiber and matrix, where the interface behavior is adopted from nanoscale MD simulations. This algorithm is used to understand damage mechanisms in adhesively bonded composite joints as a case study for the comprehensive nano to macroscale structural analysis of practical composites structures. At each length scale sources of variability are identified, characterized, and included in the multiscale modeling framework.

Materials with unprecedented properties are necessary to make dramatic changes in current and future aerospace platforms. Hybrid materials and composites are increasingly being used in aircraft and spacecraft frames; however, future platforms will require an optimal design of novel materials that enable operation in a variety of environments and produce known/predicted damage mechanisms. Nanocomposites and nanoengineered composites with CNTs have the potential to make significant improvements in strength, stiffness, fracture toughness, flame retardancy and resistance to corrosion. Therefore, these materials have generated tremendous scientific and technical interest over the past decade and various architectures are being explored for applications to light-weight airframe structures. However, the success of such materials with significantly improved performance metrics requires careful control of the parameters during synthesis and processing. Their implementation is also limited due to the lack of complete understanding of the effects the nanoparticles impart to the bulk properties of composites. It is common for computational methods to be applied to explain phenomena measured or observed experimentally. Frequently, a given phenomenon or material property is only considered to be fully understood when the associated physics has been identified through accompanying calculations or simulations.

The computationally and experimentally integrated research presented in this dissertation provides improved understanding of the mechanical behavior and response including damage and failure in CNT nanocomposites, enhancing confidence in their applications. The computations at the atomistic level helps to understand the underlying mechanochemistry and allow a systematic investigation of the complex CNT architectures and the material performance across a wide range of parameters. Simulation of the bond breakage phenomena and development of the interface to continuum scale damage captures the effects of applied loading and damage precursor and provides insight into the safety of nanoengineered composites under service loads. The validated modeling methodology is expected to be a step in the direction of computationally-assisted design and certification of novel materials, thus liberating the pace of their implementation in future applications.

A hybrid molecular dynamics (MD) simulation framework is developed to emulate mechanochemical reaction of mechanophores in epoxy-based nanocomposites. Two different force fields, a classical force field and a bond order based force field are hybridized to mimic the experimental processes from specimen preparation to mechanical loading test. Ultra-violet photodimerization for mechanophore synthesis and epoxy curing for thermoset polymer generation are successfully simulated by developing a numerical covalent bond generation method using the classical force field within the framework. Mechanical loading tests to activate mechanophores are also virtually conducted by deforming the volume of a simulation unit cell. The unit cell deformation leads to covalent bond elongation and subsequent bond breakage, which is captured using the bond order based force field. The outcome of the virtual loading test is used for local work analysis, which enables a quantitative study of mechanophore activation. Through the local work analysis, the onset and evolution of mechanophore activation indicating damage initiation and propagation are estimated; ultimately, the mechanophore sensitivity to external stress is evaluated. The virtual loading tests also provide accurate estimations of mechanical properties such as elastic, shear, bulk modulus, yield strain/strength, and Poisson’s ratio of the system. Experimental studies are performed in conjunction with the simulation work to validate the hybrid MD simulation framework. Less than 2% error in estimations of glass transition temperature (Tg) is observed with experimentally measured Tgs by use of differential scanning calorimetry. Virtual loading tests successfully reproduce the stress-strain curve capturing the effect of mechanophore inclusion on mechanical properties of epoxy polymer; comparable changes in Young’s modulus and yield strength are observed in experiments and simulations. Early damage signal detection, which is identified in experiments by observing increased intensity before the yield strain, is captured in simulations by showing that the critical strain representing the onset of the mechanophore activation occurs before the estimated yield strain. It is anticipated that the experimentally validated hybrid MD framework presented in this dissertation will provide a low-cost alternative to additional experiments that are required for optimizing material design parameters to improve damage sensing capability and mechanical properties.

In addition to the study of mechanochemical reaction analysis, an atomistic model of interphase in carbon fiber reinforced composites is developed. Physical entanglement between semi-crystalline carbon fiber surface and polymer matrix is captured by introducing voids in multiple graphene layers, which allow polymer matrix to intertwine with graphene layers. The hybrid MD framework is used with some modifications to estimate interphase properties that include the effect of the physical entanglement. The results are compared with existing carbon fiber surface models that assume that carbon fiber has a crystalline structure and hence are unable to capture the physical entanglement. Results indicate that the current model shows larger stress gradients across the material interphase. These large stress gradients increase the viscoplasticity and damage effects at the interphase. The results are important for improved prediction of the nonlinear response and damage evolution in composite materials.