Determining parameters for a system model for marine vessels becomes more difficult as the model is made more complex. Work has been done to determine the equations of motion, but not to fully define how to estimate all of the system parameters. This work utilizes a global optimization methodology for estimating the system parameters using a genetic algorithm. The optimizer uses training data sets created from a set of ship maneuvering standards to minimize the error in the 3 degree-of-freedom equations of motion. The model has been optimized using a “No Surge-Yaw” model (minimal surge coupling) and a “Full” model (all states have coupling effects to each other) to determine how well each model can be estimated. The “No Surge-Yaw” model had the best results with making a working marine vessel model. The “Full” model was difficult to optimize due to the additional parameters that had unknown, nonlinear constraints. The “No Surge-Yaw” model was compared to linearized, no coupling version of the model that is commonly used. The linearized model vastly overestimated the results in sway and yaw rate motion while the “No Surge-Yaw” captured the expected coupling dynamics that do exist. Overall, the results of this methodology did generate a set of working marine vessel parameters for an unknown, coupled-state dynamic model.
","abstract_html":"<p>Determining parameters for a system model for marine vessels becomes more difficult as the model is made more complex. Work has been done to determine the equations of motion, but not to fully define how to estimate all of the system parameters. This work utilizes a global optimization methodology for estimating the system parameters using a genetic algorithm. The optimizer uses training data sets created from a set of ship maneuvering standards to minimize the error in the 3 degree-of-freedom equations of motion. The model has been optimized using a “No Surge-Yaw” model (minimal surge coupling) and a “Full” model (all states have coupling effects to each other) to determine how well each model can be estimated. The “No Surge-Yaw” model had the best results with making a working marine vessel model. The “Full” model was difficult to optimize due to the additional parameters that had unknown, nonlinear constraints. The “No Surge-Yaw” model was compared to linearized, no coupling version of the model that is commonly used. The linearized model vastly overestimated the results in sway and yaw rate motion while the “No Surge-Yaw” captured the expected coupling dynamics that do exist. Overall, the results of this methodology did generate a set of working marine vessel parameters for an unknown, coupled-state dynamic model.</p>","abstract_has_math":false,"creators":["Schoener, Marco Alan"],"institution":null,"degree_name":"Master of Science in Mechanical Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-04-01T07:00:00Z","date_published":"2019-04-01T07:00:00Z","updated_at":"2026-07-27T19:25:52Z","subjects":["waves","dynamics","genetic algorithm","Mechanical Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/453","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Schoener, Marco Alan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Mechanical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["waves","dynamics","genetic algorithm","Mechanical Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/453"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Determining parameters for a system model for marine vessels becomes more difficult as the model is made more complex. Work has been done to determine the equations of motion, but not to fully define how to estimate all of the system parameters. This work utilizes a global optimization methodology for estimating the system parameters using a genetic algorithm. The optimizer uses training data sets created from a set of ship maneuvering standards to minimize the error in the 3 degree-of-freedom equations of motion. The model has been optimized using a “No Surge-Yaw” model (minimal surge coupling) and a “Full” model (all states have coupling effects to each other) to determine how well each model can be estimated. The “No Surge-Yaw” model had the best results with making a working marine vessel model. The “Full” model was difficult to optimize due to the additional parameters that had unknown, nonlinear constraints. The “No Surge-Yaw” model was compared to linearized, no coupling version of the model that is commonly used. The linearized model vastly overestimated the results in sway and yaw rate motion while the “No Surge-Yaw” captured the expected coupling dynamics that do exist. Overall, the results of this methodology did generate a set of working marine vessel parameters for an unknown, coupled-state dynamic model.
"]},{"key":"dc:title","label":"Title","values":["Global Estimation Methodology for Wave Adaptation Modular Vessel Dynamics Using a Genetic Algorithm"]}]}],"canonical_facts":{"dc:creator":["Schoener, Marco Alan"],"dc:description.abstract":["Determining parameters for a system model for marine vessels becomes more difficult as the model is made more complex. Work has been done to determine the equations of motion, but not to fully define how to estimate all of the system parameters. This work utilizes a global optimization methodology for estimating the system parameters using a genetic algorithm. The optimizer uses training data sets created from a set of ship maneuvering standards to minimize the error in the 3 degree-of-freedom equations of motion. The model has been optimized using a “No Surge-Yaw” model (minimal surge coupling) and a “Full” model (all states have coupling effects to each other) to determine how well each model can be estimated. The “No Surge-Yaw” model had the best results with making a working marine vessel model. The “Full” model was difficult to optimize due to the additional parameters that had unknown, nonlinear constraints. The “No Surge-Yaw” model was compared to linearized, no coupling version of the model that is commonly used. The linearized model vastly overestimated the results in sway and yaw rate motion while the “No Surge-Yaw” captured the expected coupling dynamics that do exist. Overall, the results of this methodology did generate a set of working marine vessel parameters for an unknown, coupled-state dynamic model.
"],"dc:identifier":["https://commons.erau.edu/edt/453"],"dc:subject":["waves","dynamics","genetic algorithm","Mechanical Engineering"],"dc:title":["Global Estimation Methodology for Wave Adaptation Modular Vessel Dynamics Using a Genetic Algorithm"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Mechanical Engineering"]},"updated_at":"2026-07-27T19:25:52Z"}