{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/122613"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/122613","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Quantification of extreme event statistics in ship design","abstract":"Increased operational demands on Navy vessels extend time at sea and service life, making the accurate prediction of catastrophic failures increasingly challenging. The high value of these capital assets puts great pressure on designers and decision-makers as they work towards preventing such failures while balancing both engineering and material cost. The current method for the quantification of extreme events is direct Monte Carlo simulation supplemented by complex statistical models. When such models are not sufficiently bound by physics-based simulation, the noise of statistical uncertainty quickly overpowers the response predictions for rare events. This thesis builds on previous work at the MIT Stochastic Analysis and Non-linear Dynamics (SAND) lab for the quantification of extreme events using wave groups. By separating the event probability from the physics models, we are able to capture rare events in ship motion and loading conditions for a modest computational cost. Improvements to the wave groups methodology ensured the slope and amplitude of the incident waves reflected the waves encountered in a given wave spectrum. The remaining discussion explores the value of a near-real-time risk analysis tools in reference to ship design and ship operations, with unique application to Navy and commercial vessels.","abstract_html":"Increased operational demands on Navy vessels extend time at sea and service life, making the accurate prediction of catastrophic failures increasingly challenging. The high value of these capital assets puts great pressure on designers and decision-makers as they work towards preventing such failures while balancing both engineering and material cost. The current method for the quantification of extreme events is direct Monte Carlo simulation supplemented by complex statistical models. When such models are not sufficiently bound by physics-based simulation, the noise of statistical uncertainty quickly overpowers the response predictions for rare events. This thesis builds on previous work at the MIT Stochastic Analysis and Non-linear Dynamics (SAND) lab for the quantification of extreme events using wave groups. By separating the event probability from the physics models, we are able to capture rare events in ship motion and loading conditions for a modest computational cost. Improvements to the wave groups methodology ensured the slope and amplitude of the incident waves reflected the waves encountered in a given wave spectrum. The remaining discussion explores the value of a near-real-time risk analysis tools in reference to ship design and ship operations, with unique application to Navy and commercial vessels.","abstract_has_math":false,"creators":["Rathore, Uditbhan S."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Mechanical Engineering","school":null,"contributors":[],"advisors":["Themistoklis P. Sapsis and Bryan R. Moser."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:16Z","subjects":["Mechanical Engineering.","System Design and Management Program.","Engineering and Management Program."],"languages":["eng"],"rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/122613","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Themistoklis P. Sapsis and Bryan R. Moser."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Mechanical Engineering","MechE","System Design and Management Program","SysDes","Massachusetts Institute of Technology. Engineering and Management Program"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Department of Mechanical Engineering.","System Design and Management Program.","Massachusetts Institute of Technology. Engineering and Management Program."]},{"key":"dc:creator","label":"Author","values":["Rathore, Uditbhan S."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-10-16T21:32:21Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-10-16T21:32:21Z"]},{"key":"dc:date.issued","label":"Date","values":["2019"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Mechanical Engineering.","System Design and Management Program.","Engineering and Management Program."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/122613"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: Nav. E., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019","Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2019","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 85-86)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Increased operational demands on Navy vessels extend time at sea and service life, making the accurate prediction of catastrophic failures increasingly challenging. The high value of these capital assets puts great pressure on designers and decision-makers as they work towards preventing such failures while balancing both engineering and material cost. The current method for the quantification of extreme events is direct Monte Carlo simulation supplemented by complex statistical models. When such models are not sufficiently bound by physics-based simulation, the noise of statistical uncertainty quickly overpowers the response predictions for rare events. This thesis builds on previous work at the MIT Stochastic Analysis and Non-linear Dynamics (SAND) lab for the quantification of extreme events using wave groups. By separating the event probability from the physics models, we are able to capture rare events in ship motion and loading conditions for a modest computational cost. Improvements to the wave groups methodology ensured the slope and amplitude of the incident waves reflected the waves encountered in a given wave spectrum. The remaining discussion explores the value of a near-real-time risk analysis tools in reference to ship design and ship operations, with unique application to Navy and commercial vessels."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Nav. E.","S.M. in Engineering and Management"]},{"key":"dc:title","label":"Title","values":["Quantification of extreme event statistics in ship design"]}]}],"canonical_facts":{"dc:contributor.advisor":["Themistoklis P. Sapsis and Bryan R. Moser."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Mechanical Engineering","MechE","System Design and Management Program","SysDes","Massachusetts Institute of Technology. Engineering and Management Program"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Mechanical Engineering.","System Design and Management Program.","Massachusetts Institute of Technology. Engineering and Management Program."],"dc:creator":["Rathore, Uditbhan S."],"dc:date.accessioned":["2019-10-16T21:32:21Z"],"dc:date.available":["2019-10-16T21:32:21Z"],"dc:date.issued":["2019"],"dc:description":["Thesis: Nav. E., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019","Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2019","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 85-86)."],"dc:description.abstract":["Increased operational demands on Navy vessels extend time at sea and service life, making the accurate prediction of catastrophic failures increasingly challenging. The high value of these capital assets puts great pressure on designers and decision-makers as they work towards preventing such failures while balancing both engineering and material cost. The current method for the quantification of extreme events is direct Monte Carlo simulation supplemented by complex statistical models. When such models are not sufficiently bound by physics-based simulation, the noise of statistical uncertainty quickly overpowers the response predictions for rare events. This thesis builds on previous work at the MIT Stochastic Analysis and Non-linear Dynamics (SAND) lab for the quantification of extreme events using wave groups. By separating the event probability from the physics models, we are able to capture rare events in ship motion and loading conditions for a modest computational cost. Improvements to the wave groups methodology ensured the slope and amplitude of the incident waves reflected the waves encountered in a given wave spectrum. The remaining discussion explores the value of a near-real-time risk analysis tools in reference to ship design and ship operations, with unique application to Navy and commercial vessels."],"dc:description.degree":["Nav. E.","S.M. in Engineering and Management"],"dc:identifier.uri":["https://hdl.handle.net/1721.1/122613"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Mechanical Engineering.","System Design and Management Program.","Engineering and Management Program."],"dc:title":["Quantification of extreme event statistics in ship design"],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:21:16Z"}