Study on the Design of an Underwater Chain Trencher via a Genetic Algorithm SCIE SCOPUS

DC Field Value Language Kim, Jaebum - Kwon, O. Soon - Nguyen Le Dang Hai - Ko, Jin Hwan - 2021-03-17T08:15:15Z - 2021-03-17T08:15:15Z - 2021-03-17T08:15:15Z - 2021-03-17T08:15:15Z - 2020-02-04 - 2019-12 -
dc.identifier.issn 2077-1312 -
dc.identifier.uri -
dc.description.abstract In this study, a genetic algorithm (GA) with an analytic model is adopted to conduct multi-objective optimization for design of an underwater chain trencher. The optimization problem is defined as minimizing a product of the chain power and weight subject to the uniaxial compressive strength, coefficient of traction, bar length (L), nose radius (R) and ratio of the chipping depth over the spacing (l/S), of which the ranges are determined based on the specifications of commercial trenchers satisfying established performance requirements and previous parametric studies. It is found that an optimal design of the GA was obtained with L and l/S close to their low bound and R far from its low bound while that of a simple parametric analysis was acquired with the three parameters close to their low bounds. Moreover, in the most severe soft rock and traction conditions, the power and weight in the optimal design obtained by the GA are turn to be within the feasible ranges of targeted commercial trenchers. -
dc.description.uri 1 -
dc.language English -
dc.publisher MDPI -
dc.title Study on the Design of an Underwater Chain Trencher via a Genetic Algorithm -
dc.type Article -
dc.citation.volume 7 -
dc.citation.number 12 -
dc.contributor.alternativeName 권오순 -
dc.identifier.bibliographicCitation JOURNAL OF MARINE SCIENCE AND ENGINEERING, v.7, no.12 -
dc.identifier.doi 10.3390/jmse7120429 -
dc.identifier.scopusid 2-s2.0-85079552015 -
dc.identifier.wosid 000506654700009 -
dc.type.docType Article -
dc.description.journalClass 1 -
dc.description.isOpenAccess N -
dc.subject.keywordAuthor chain trenching machine -
dc.subject.keywordAuthor analytical model -
dc.subject.keywordAuthor multi-objective optimization -
dc.subject.keywordAuthor genetic algorithm -
dc.relation.journalWebOfScienceCategory Oceanography -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.relation.journalResearchArea Oceanography -
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Coastal & Ocean Engineering Division > Maritime Robotics Test and Evaluation Center > 1. Journal Articles
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