{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/34532"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/34532","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Multi-Objective Design Optimization Considering Uncertainty in a Multi-Disciplinary Ship Synthesis Model","abstract":"Multi-disciplinary ship synthesis models and multi-objective optimization techniques are increasingly being used in ship design. Multi-disciplinary models allow designers to break away from the traditional design spiral approach and focus on searching the design space for the best overall design instead of the best discipline-specific design. Complex design problems such as these often have high levels of uncertainty associated with them, and since most optimization algorithms tend to push solutions to constraint boundaries, the calculated \"best\" solution might be infeasible if there are minor uncertainties related to the model or problem definition. Consequently, there is a need to address uncertainty in optimization problems to produce effective and reliable results. This thesis focuses on adding a third objective, uncertainty, to the effectiveness and cost objectives already present in a multi-disciplinary ship synthesis model. Uncertainty is quantified using a \"confidence of success\" (CoS) calculation based on the mean value method. CoS is the probability that a design will satisfy all constraints and meet performance objectives. This work proves that the CoS concept can be applied to synthesis models to estimate uncertainty early in the design process. Multiple sources of uncertainty are realistically quantified and represented in the model in order to investigate their relative importance to the overall uncertainty. This work also presents methods to encourage a uniform distribution of points across the Pareto front. With a well defined front, designs can be selected and refined using a gradient based optimization algorithm to optimize a single objective while holding the others fixed.","abstract_html":"Multi-disciplinary ship synthesis models and multi-objective optimization techniques are increasingly being used in ship design. Multi-disciplinary models allow designers to break away from the traditional design spiral approach and focus on searching the design space for the best overall design instead of the best discipline-specific design. Complex design problems such as these often have high levels of uncertainty associated with them, and since most optimization algorithms tend to push solutions to constraint boundaries, the calculated "best" solution might be infeasible if there are minor uncertainties related to the model or problem definition. Consequently, there is a need to address uncertainty in optimization problems to produce effective and reliable results. This thesis focuses on adding a third objective, uncertainty, to the effectiveness and cost objectives already present in a multi-disciplinary ship synthesis model. Uncertainty is quantified using a "confidence of success" (CoS) calculation based on the mean value method. CoS is the probability that a design will satisfy all constraints and meet performance objectives. This work proves that the CoS concept can be applied to synthesis models to estimate uncertainty early in the design process. Multiple sources of uncertainty are realistically quantified and represented in the model in order to investigate their relative importance to the overall uncertainty. This work also presents methods to encourage a uniform distribution of points across the Pareto front. With a well defined front, designs can be selected and refined using a gradient based optimization algorithm to optimize a single objective while holding the others fixed.","abstract_has_math":false,"creators":["Good, Nathan Andrew"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Aerospace and Ocean Engineering","degree_department":"Aerospace and Ocean Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Neu, Wayne L."],"committee_members":["Hughes, Owen F.","Brown, Alan J."],"year":2006,"date_issued":"2006-08-07","date_published":"2006-08-07","updated_at":"2026-07-22T22:18:57Z","subjects":["Mean Value Method","criteria","probability","Pareto optimal","uncertainty","Multi-Disciplinary Optimization"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-08142006-175705"],"render_values":[{"text":"etd-08142006-175705","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/34532","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Neu, Wayne L."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Hughes, Owen F.","Brown, Alan J."]},{"key":"dc:contributor.department","label":"Department","values":["Aerospace and Ocean Engineering"]},{"key":"dc:creator","label":"Author","values":["Good, Nathan Andrew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:43:26Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:43:26Z","2006-08-17"]},{"key":"dc:date.issued","label":"Date","values":["2006-08-07"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace and Ocean Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Mean Value Method","criteria","probability","Pareto optimal","uncertainty","Multi-Disciplinary Optimization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-08142006-175705"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/34532"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Multi-disciplinary ship synthesis models and multi-objective optimization techniques are increasingly being used in ship design. Multi-disciplinary models allow designers to break away from the traditional design spiral approach and focus on searching the design space for the best overall design instead of the best discipline-specific design. Complex design problems such as these often have high levels of uncertainty associated with them, and since most optimization algorithms tend to push solutions to constraint boundaries, the calculated \"best\" solution might be infeasible if there are minor uncertainties related to the model or problem definition. Consequently, there is a need to address uncertainty in optimization problems to produce effective and reliable results. This thesis focuses on adding a third objective, uncertainty, to the effectiveness and cost objectives already present in a multi-disciplinary ship synthesis model. Uncertainty is quantified using a \"confidence of success\" (CoS) calculation based on the mean value method. CoS is the probability that a design will satisfy all constraints and meet performance objectives. This work proves that the CoS concept can be applied to synthesis models to estimate uncertainty early in the design process. Multiple sources of uncertainty are realistically quantified and represented in the model in order to investigate their relative importance to the overall uncertainty. This work also presents methods to encourage a uniform distribution of points across the Pareto front. With a well defined front, designs can be selected and refined using a gradient based optimization algorithm to optimize a single objective while holding the others fixed."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Multi-Objective Design Optimization Considering Uncertainty in a Multi-Disciplinary Ship Synthesis Model"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Neu, Wayne L."],"dc:contributor.committeemember":["Hughes, Owen F.","Brown, Alan J."],"dc:contributor.department":["Aerospace and Ocean Engineering"],"dc:creator":["Good, Nathan Andrew"],"dc:date.accessioned":["2014-03-14T20:43:26Z"],"dc:date.available":["2014-03-14T20:43:26Z","2006-08-17"],"dc:date.issued":["2006-08-07"],"dc:description.abstract":["Multi-disciplinary ship synthesis models and multi-objective optimization techniques are increasingly being used in ship design. Multi-disciplinary models allow designers to break away from the traditional design spiral approach and focus on searching the design space for the best overall design instead of the best discipline-specific design. Complex design problems such as these often have high levels of uncertainty associated with them, and since most optimization algorithms tend to push solutions to constraint boundaries, the calculated \"best\" solution might be infeasible if there are minor uncertainties related to the model or problem definition. Consequently, there is a need to address uncertainty in optimization problems to produce effective and reliable results. This thesis focuses on adding a third objective, uncertainty, to the effectiveness and cost objectives already present in a multi-disciplinary ship synthesis model. Uncertainty is quantified using a \"confidence of success\" (CoS) calculation based on the mean value method. CoS is the probability that a design will satisfy all constraints and meet performance objectives. This work proves that the CoS concept can be applied to synthesis models to estimate uncertainty early in the design process. Multiple sources of uncertainty are realistically quantified and represented in the model in order to investigate their relative importance to the overall uncertainty. This work also presents methods to encourage a uniform distribution of points across the Pareto front. With a well defined front, designs can be selected and refined using a gradient based optimization algorithm to optimize a single objective while holding the others fixed."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-08142006-175705"],"dc:identifier.uri":["http://hdl.handle.net/10919/34532"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Mean Value Method","criteria","probability","Pareto optimal","uncertainty","Multi-Disciplinary Optimization"],"dc:title":["Multi-Objective Design Optimization Considering Uncertainty in a Multi-Disciplinary Ship Synthesis Model"],"dc:type":["Thesis"],"thesis:degree_discipline":["Aerospace and Ocean Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:18:57Z"}