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Themistoklis P. Sapsis and Bryan R. Moser. Rathore, Uditbhan S. Massachusetts Institute of Technology. Department of Mechanical Engineering. System Design and Management Program. Massachusetts Institute of Technology. Engineering and Management Program. Massachusetts Institute of Technology. Department of Mechanical Engineering Massachusetts Institute of Technology. Engineering and Management Program 2019-10-16T21:32:21Z 2019-10-16T21:32:21Z 2019 2019 2019 https://hdl.handle.net/1721.1/122613 1119389989 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). 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. by Uditbhan S. Rathore. Nav.E. S.M. in Engineering and Management Nav.E. Massachusetts Institute of Technology, Department of Mechanical Engineering S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program 86 pages eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Mechanical Engineering. System Design and Management Program. Engineering and Management Program. Quantification of extreme event statistics in ship design Thesis application/pdf 2019-10-16T21:32:19Z Publication Master MechE SysDes unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="4b019842-fff5-480d-8a36-4d3fbef61860"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Quantification of extreme event statistics in ship design</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Rathore, Uditbhan S.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Mechanical Engineering.</Keyword> <Keyword>System Design and Management Program.</Keyword> <Keyword>Engineering and Management Program.</Keyword> <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> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>