{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/54818"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/54818","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"System reliability optimization of aircraft wings","abstract":"System reliability based design of aircraft wings is studied. A wing of a light commuter aircraft designed according to the FAA regulations is compared with one designed by system reliability optimization. Both the level III, and the advanced first order, second moment (AFOSM) method are employed to evaluate the probability of failure of each failure element of the system representing the wing. In the level III method the statistical correlation between failure modes is neglected. The AFOSM method allows to evaluate the sensitivity derivatives of the system safety index analytically. Furthermore, it accounts for the statistical correlation between failure modes. The results demonstrate the potential of stochastic optimization, and the importance of accounting for the statistical correlation between failure modes. Finally, it is shown that the problem associated with discontinuity of sensitivity derivatives, encountered when using second order Ditlevsen upper bounds to estimate the system failure probability, is circumvented if a penalty function method is used for optimization.","abstract_html":"System reliability based design of aircraft wings is studied. A wing of a light commuter aircraft designed according to the FAA regulations is compared with one designed by system reliability optimization. Both the level III, and the advanced first order, second moment (AFOSM) method are employed to evaluate the probability of failure of each failure element of the system representing the wing. In the level III method the statistical correlation between failure modes is neglected. The AFOSM method allows to evaluate the sensitivity derivatives of the system safety index analytically. Furthermore, it accounts for the statistical correlation between failure modes. The results demonstrate the potential of stochastic optimization, and the importance of accounting for the statistical correlation between failure modes. Finally, it is shown that the problem associated with discontinuity of sensitivity derivatives, encountered when using second order Ditlevsen upper bounds to estimate the system failure probability, is circumvented if a penalty function method is used for optimization.","abstract_has_math":false,"creators":["Yang, Ju-Sung"],"institution":"Virginia Polytechnic Institute and State University","degree_name":"Ph. 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Both the level III, and the advanced first order, second moment (AFOSM) method are employed to evaluate the probability of failure of each failure element of the system representing the wing. In the level III method the statistical correlation between failure modes is neglected. The AFOSM method allows to evaluate the sensitivity derivatives of the system safety index analytically. Furthermore, it accounts for the statistical correlation between failure modes. The results demonstrate the potential of stochastic optimization, and the importance of accounting for the statistical correlation between failure modes. Finally, it is shown that the problem associated with discontinuity of sensitivity derivatives, encountered when using second order Ditlevsen upper bounds to estimate the system failure probability, is circumvented if a penalty function method is used for optimization."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph. 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Both the level III, and the advanced first order, second moment (AFOSM) method are employed to evaluate the probability of failure of each failure element of the system representing the wing. In the level III method the statistical correlation between failure modes is neglected. The AFOSM method allows to evaluate the sensitivity derivatives of the system safety index analytically. Furthermore, it accounts for the statistical correlation between failure modes. The results demonstrate the potential of stochastic optimization, and the importance of accounting for the statistical correlation between failure modes. Finally, it is shown that the problem associated with discontinuity of sensitivity derivatives, encountered when using second order Ditlevsen upper bounds to estimate the system failure probability, is circumvented if a penalty function method is used for optimization."],"dc:description.degree":["Ph. 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