Ship Hull Form Design Intelligence with Generative Technology and Evolutionary Algorithms

Ship Hull Form Design Intelligence with Generative Technology and Evolutionary Algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Ship Hull Form Design Intelligence with Generative Technology and Evolutionary Algorithms shunhuai chen, long zheng, hao wang, feichi gu, hao wang, Kun Ban, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7285294/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract An efficient hull form design method is crucial for accelerating the early-stage conceptual design of ships, potentially saving significant development time and billions of dollars in cost. Current hull design approaches rely heavily on the experience of designers and reference to parent ships. Although there are some innovations based on parametric design methods, they still require a series of manual interventions. Here, we present ShipDMaster, a ship hull form generation framework that integrates generative techniques and evolutionary algorithms. ShipDMaster consists of two main components: ShipInfoGAN, which facilitates rapid generation of hull forms, and an evolutionary computation module, which enhances the quality of the generated designs. ShipDMaster is suitable for the rapid exploration of new parent hull forms during the conceptual design stage. Moreover, it can generate design solutions that meet user-defined principal dimensions and shape constraints. With the support of evolutionary algorithms, ShipDMaster can also perform hull optimization tasks. Through extensive experiments, we demonstrate that ShipDMaster effectively handles hull form design tasks. In addition, the results show that, in terms of resistance performance, the hull forms designed by ShipDMaster are potentially comparable to those created by human designers. Physical sciences/Engineering/Mechanical engineering Physical sciences/Mathematics and computing/Computational science Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryMaterialsXXX.csv Supplementary Materials Ⅱ SupplementaryMaterialsXXX.pdf Supplementary Materials Ⅰ Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7285294","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":497298809,"identity":"aec4ac89-a0ef-471b-83d7-ff1f3257ccf7","order_by":0,"name":"shunhuai 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