Two-parameter Hydrofoil Hydrodynamic Surrogate Model and Cross-Section Prediction

Two-parameter Hydrofoil Hydrodynamic Surrogate Model and Cross-Section Prediction | 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 Research Article Two-parameter Hydrofoil Hydrodynamic Surrogate Model and Cross-Section Prediction Fangwen Hong, Chao-sheng Zheng, Xiang-jie Yao, Xu Cheng, Jian Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9530345/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Aiming at the pain points that the traditional prediction of hydrofoil hydrodynamic performance relies on CFD numerical simulation and model tests, which is time-consuming, costly, and difficult to support the rapid iterative design of engineering, this paper relies on the classic complex function conformal transformation theory of the Zhukovsky profile to construct a standardized hydrofoil sample library with two-parameter geometric control, and adopts the random forest ensemble learning algorithm to establish an surrogate model for hydrodynamic performance. Meanwhile, aiming at the non-Zhukovsky profile hydrofoils commonly used in engineering, a cross-section performance extension method combining geometric nearest neighbor matching and thin airfoil theory correction is proposed to break through the generalization limitation of the single-section surrogate model. Through CFD numerical calculation, sample data of lift coefficient and drag coefficient of 143 groups of Zhukovsky profile hydrofoils with different thicknesses and cambers in the angle of attack range of 0°~7° are obtained to complete the training and verification of the model. The model determination coefficient R2>0.99 and the root mean square error RMSE<0.01. For typical NACA profile hydrofoils, second-level hydrodynamic prediction is realized, which verifies the high efficiency of the method. This method can greatly reduce the hydrodynamic analysis cost of hydrofoils and propeller blade profiles, and provide intelligent technical support for the rapid optimal design of equipment such as ship underwater propellers and hydrofoil crafts. Zhukovsky profile hydrofoil surrogate model cross-profile extension Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 May, 2026 Reviewers invited by journal 01 May, 2026 Editor assigned by journal 30 Apr, 2026 First submitted to journal 26 Apr, 2026 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-9530345","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":632785418,"identity":"498d04e1-5693-465e-ac01-409acd18d10b","order_by":0,"name":"Fangwen 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