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Master of Science in Aerospace Engineering

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https://hdl.handle.net/1853/73269
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    A methodology for conducting design trades related to advanced in-space assembly
    (Georgia Institute of Technology, 2018-12-07) Jara de Carvalho Vale de Almeida, Lourenco
    In the decades since the end of the Apollo program, manned space missions have been confined to Low Earth Orbit. Today, ambitious efforts are underway to return astronauts to the surface of the Moon, and eventually reach Mars. Technical challenges and dangers to crew health and well-being will require innovative solutions. The use of In-Space Assembly (ISA) can provide critical new capabilities, by freeing designs from the size limitations of launch vehicles. ISA can be performed using different strategies. The current state-of-the-art strategy is to dock large modules together. Future technologies, such as welding in space, will unlock more advanced strategies. Advanced assembly strategies deliver smaller component pieces to orbit in highly efficient packaging but require lengthy assembly tasks to be performed in space. The choice of assembly strategy impacts the cost and duration of the entire mission. As a rule, simpler strategies require more deliveries, increasing costs, while advanced strategies require more assembly tasks, increasing time. The effects of these design choices must be modeled in order to conduct design trades. A methodology to conduct these design trades is presented. It uses a model of the logistics involved in assembling a space system, including deliveries and assembly tasks. The model employs a network formulation, where the pieces of a structure must flow from their initial state to a final assembly state, via arcs representing deliveries and assembly tasks. By comparing solutions obtained under different scenarios, additional design trades can be performed. This methodology is applied to the case of an Artificial Gravity Space Station. Results for the assembly of this system are obtained for a baseline scenario and compared with results after varying parameters such as the delivery and storage capacity. The comparison reveals the sensitivities of the assembly process to each parameter and the benefits that can be gained from certain improvements, demonstrating the effectiveness of the methodology.
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    A nonparametric-based approach on the propagation of imprecise probabilities due to small datasets
    (Georgia Institute of Technology, 2018-04-25) Gao, Zhenyu
    Quantification of uncertainty (UQ) is typically done by the use of precise probabilities, which requires a very high level of precision and consistency of information for the uncertain sources, and is rarely available for actual engineering applications. For better accuracy in the UQ process, greater flexibility in accommodating distributions for uncertain sources is needed to base inferences on weaker assumptions and avoid introducing unwarranted information. Latest literatures proposed a parametric-based approach for the propagation of uncertainty created by lack of sufficient statistical data, yet still has some notable limitations and constraints. This work proposes a nonparametric-based approach that facilitates the propagation of uncertainty in the small dataset case. The first part of this work uses Kernel Density Estimation (KDE) and Bootstrap to estimate the probability density function of a random variable based on small datasets. As a result, two types of sampling densities for propagating uncertainty are generated: an optimal sampling density representing the best estimate of the true density, and a maximum variance density representing risk and uncertainty that is inherent in small datasets. The second part extends the first part, to generate two-dimensional nonparametric density estimates and capture dependencies among variables. After a process to confirm the correlation among the variables based on small datasets, Copulas and the Sklar's Theorem are used to link the marginal nonparametric densities and create joint densities. By propagating the joint densities for dependent variables, researchers can prevent uncertainty in the outputs from being underestimated or overestimated. The effectiveness of the nonparametric density estimation methods is tested by selected test cases with different statistical characteristics. A complete uncertainty propagation test through a complex systems model is also conducted. Finally, the nonparametric-based methods developed in this thesis are applied to a challenging problem in aviation environmental impact analysis.
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    A physics based robust methodology for aerodynamic design analysis and optimization
    (Georgia Institute of Technology, 2000-08) Jimeno, Jesus