Theses and Dissertations - Department of Aerospace Engineering and Mechanics

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Browsing Theses and Dissertations - Department of Aerospace Engineering and Mechanics by Author "Barkey, Mark"

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    Atomistic modeling and structure-property relationship of topologically accurate complex nanotube junction architectures
    (University of Alabama Libraries, 2020-08) Nakarmi, Sushan; Barkey, Mark; Unnikrishnan, Vinu; University of Alabama Tuscaloosa
    Carbon nanotubes have remarkable material properties and are ideal for different space applications including thermal management devices, light-weight mechanical shock absorbers, and fiber-reinforced composites. Nanotube junctions, which are the interconnections of carbon nanotubes, have properties different from the pristine structures and are promising materials for constructing unit blocks with excellent material properties. However, widespread application of the junctions and nanostructures is limited due to the lack of understanding of their mechanical, thermal, and electronic properties. The overall objective of the current research is to provide a computational methodology to construct atomistic models of nanostructures and study their thermal and mechanical properties under different operating conditions. In the first part of the research, the topologically accurate atomistic models of the junctions are created using a novel CAD-based remeshing and optimization strategies. The most energetically stable configurations are chosen to build 3D architectures, thus, providing an economical way to construct complex and larger dimension nanostructures. The created macro-structures can be used directly in the atomistic simulations to study their structure-property relationships. In this dissertation, the thermal and mechanical characterization of pristine nanotubes and complex nanotube multiterminal junctions have been studied using molecular dynamics (MD) simulation. At the nanoscale, the thermal conductivity of nanotube is found to be dependent on size, strain, temperature, and defects. The effects of each of these parameters on the thermal transport of nanostructures have been determined using MD. This is followed by the comparative study of the phonon density of states and phonon dispersion relations of different configurations. The study provides guidelines for creating nanotube heat transfer devices with desired thermal specifications. In addition to being highly conductive, nanotubes and junctions have very high strength and modulus. Although an extensive amount of research is available with the characterization of the pristine nanotubes, there lacks a proper understanding of the mechanical characteristics of the complex structures (multi-terminal junctions and micro-blocks). With the atomistic models of these structures created, the tensile and compressive strengths of such complex architectures have been presented. These computational models will provide the much needed next step for the realization of nanotube junctions for the industrial applications.
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    Efficient algorithms for uncertainty quantification using polynomial chaos expansion and its applications to composite structures
    (University of Alabama Libraries, 2019) Thapa, Mishal; Mulani, Sameer B.; University of Alabama Tuscaloosa
    Uncertainty Quantification (UQ) deals with the study of variation in the response due to the presence of uncertainties in input parameters and governing models. Among the prevalent probabilistic techniques for UQ, non-intrusive Polynomial Chaos Expansion (PCE) has become more popular recently due to its mean square convergence property and ability to integrate deterministic codes as black-box. However, the number of basis terms in the expansion increases exponentially with the number of random inputs - ‘curse of dimensionality,’ and demands a huge number of function evaluations. Hence, this dissertation has attempted to extensively explore new robust algorithms for PCE while maintaining a proper balance between accuracy and computational efficiency. At first, a new non-intrusive method for PCE called Polynomial Chaos Decomposition with Differentiation (PCDD) is developed. The PCDD utilizes higher-order sensitivities of the responses and requires samples equal to the number of basis terms only. Secondly, the PCDD is utilized to develop a stochastic multi-scale modeling framework for composite structures since the response of composites is hugely influenced by the uncertainties existing at different scales such as micro-scale and macro-scale. Another framework for stochastic progressive failure analysis (PFA) of composites is also developed that allows performing global sensitivity analysis (GSA) to identify the relative importance of random inputs as a post-processing step. To further reduce the number of samples and make the stochastic problem more tractable, an adaptive L2-minimization algorithm that allows basis adaptivity along with sequential adaptive sampling is developed. Finally, an adaptive algorithm to obtain sparse PCE models with L1-minimization and sequential sampling is also proposed for high-dimensional problems. The L1-minimization is capable of solving the under-determined system when the number of samples is minuscule. It is also advantageous in terms of computational storage and memory because of its ability to provide a sparse solution. In general, the overarching goal of obtaining high-fidelity stochastic response models while maintaining a balance between accuracy and computational cost was successfully achieved by the novel algorithms developed in this dissertation. Furthermore, the invaluable information obtained with PCE for composite structures highlighted the benefits of its implementation in engineering problems.