Research on the parametric design of marine nuclear-powered turbine and the multi-objective intelligent optimization of aerothermodynamics performance under variable working conditions

Research on the parametric design of marine nuclear-powered turbine and the multi-objective intelligent optimization of aerothermodynamics performance under variable working conditions | 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 Research on the parametric design of marine nuclear-powered turbine and the multi-objective intelligent optimization of aerothermodynamics performance under variable working conditions Lei. Zhang, Guo. Bing. Chen, Yu-ang. Shi, LuoTao. Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6236801/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract In order to improve the aerothermodynamics performance of the marine nuclear-powered turbine and control the exhaust humidity of the blade grid, thereby improving the operating efficiency and power of the turbine and ensuring safety. We propose a method for parameterized reconstruction of steam turbine blade profiles based on their geometric parameters using a coordinate equation developed based on the third-order Bezier curve. By combining the blade parameterized reconstruction method with a Kriging approximation model and a multi-objective genetic algorithm (GA), we developed an optimized system for thermodynamic performance in turbines. The optimization objective was the cascade core thermal parameters of steam turbine under multiple operating conditions. The design parameters were the geometric parameters of the parameterized blade profile. Based on the calculation results of wet steam non-equilibrium condensate flow of steam turbine, the optimization method and process of multi-objective thermodynamic performance of steam turbine blades based on Kriging model were proposed. Then, we executed parameterized reconstruction of a Dykas planar cascade and a steam turbine 3D cascade to achieve multi-parameter, multi-condition design optimization of planar and 3D cascades. Using the proposed novel method, the isentropic efficiency and stage power of steam turbine cascades at variable operating conditions were enhanced, thermodynamic parameters (e.g., velocity, temperature, and pressure) were more homogeneously distributed, and overload condition sites showed more significant improvements. Thus, the proposed method achieved multi-condition, multi-constraint thermodynamic performance design optimization of wet steam turbine blades, thereby providing insights for intelligent design optimization and operation of wet steam turbine cascades. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing turbine thermodynamic performance parameterized reconstruction wet steam design optimization variable conditions Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 15 Apr, 2025 Reviews received at journal 15 Apr, 2025 Reviews received at journal 05 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers invited by journal 26 Mar, 2025 Editor assigned by journal 26 Mar, 2025 Editor invited by journal 26 Mar, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 16 Mar, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6236801","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":438800574,"identity":"3bde33de-1888-4089-8938-79aa23e20f31","order_by":0,"name":"Lei. 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We propose a method for parameterized reconstruction of steam turbine blade profiles based on their geometric parameters using a coordinate equation developed based on the third-order Bezier curve. By combining the blade parameterized reconstruction method with a Kriging approximation model and a multi-objective genetic algorithm (GA), we developed an optimized system for thermodynamic performance in turbines. The optimization objective was the cascade core thermal parameters of steam turbine under multiple operating conditions. The design parameters were the geometric parameters of the parameterized blade profile. Based on the calculation results of wet steam non-equilibrium condensate flow of steam turbine, the optimization method and process of multi-objective thermodynamic performance of steam turbine blades based on Kriging model were proposed. Then, we executed parameterized reconstruction of a Dykas planar cascade and a steam turbine 3D cascade to achieve multi-parameter, multi-condition design optimization of planar and 3D cascades. Using the proposed novel method, the isentropic efficiency and stage power of steam turbine cascades at variable operating conditions were enhanced, thermodynamic parameters (e.g., velocity, temperature, and pressure) were more homogeneously distributed, and overload condition sites showed more significant improvements. Thus, the proposed method achieved multi-condition, multi-constraint thermodynamic performance design optimization of wet steam turbine blades, thereby providing insights for intelligent design optimization and operation of wet steam turbine cascades.\u003c/p\u003e","manuscriptTitle":"Research on the parametric design of marine nuclear-powered turbine and the multi-objective intelligent optimization of aerothermodynamics performance under variable working conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 14:53:37","doi":"10.21203/rs.3.rs-6236801/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-15T10:13:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-15T08:42:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-05T11:39:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155984774449965259008404107829021249148","date":"2025-03-31T06:57:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135583328416399182405911520405671416288","date":"2025-03-26T11:00:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-26T08:02:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-26T07:58:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-26T05:31:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-24T08:03:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-16T09:23:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38361d74-5873-49b7-b459-bae95dc2ce2f","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46723810,"name":"Physical sciences/Energy science and technology"},{"id":46723811,"name":"Physical sciences/Engineering"},{"id":46723812,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-07-14T16:03:26+00:00","versionOfRecord":{"articleIdentity":"rs-6236801","link":"https://doi.org/10.1038/s41598-025-04520-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-10 15:57:45","publishedOnDateReadable":"July 10th, 2025"},"versionCreatedAt":"2025-04-10 14:53:37","video":"","vorDoi":"10.1038/s41598-025-04520-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-04520-4","workflowStages":[]},"version":"v1","identity":"rs-6236801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6236801","identity":"rs-6236801","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}