Structural topology optimization method based on geometry shape parameters

Structural topology optimization method based on geometry shape parameters | 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 Structural topology optimization method based on geometry shape parameters Zijun Wu, Renbin Xiao, Jun Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7968804/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2026 Read the published version in Engineering with Computers → Version 1 posted 12 You are reading this latest preprint version Abstract This work presents a geometric feature-based structural topology optimization approach that directly obtains topological configurations using the boundaries of basic geometry. Here, we assume that the topological configuration is a combination of a set of basic geometries defined by the same Non-Uniform Rational B-spline (NURBS) parameters. Within the design domain, the geometric boundary distribution position field is delineated through the utilization of mesh node and geometry boundary normal vectors. The explicit geometry mapping function is constructed by integrating the field values of all the boundaries within the geometry, which allows for the calculation of the proportion of mesh occupied by the geometries using step function. The relative density of each mesh can then be calculated. This allows the equations to be iterated and complex design results to be obtained. In the optimization process, the design variables are the geometric parameters of each individual geometry. And the Method of Moving Asymptotes (MMA) method is selected to update these design variables. Numerical examples are shown to validate the correctness and superiority of this proposed method. Topology optimization Fundamental geometry feature projection NURBS Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2026 Read the published version in Engineering with Computers → Version 1 posted Editorial decision: Revision requested 05 Jan, 2026 Reviews received at journal 22 Dec, 2025 Reviews received at journal 03 Dec, 2025 Reviews received at journal 20 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers invited by journal 14 Nov, 2025 Editor assigned by journal 29 Oct, 2025 Submission checks completed at journal 29 Oct, 2025 First submitted to journal 28 Oct, 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-7968804","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549840877,"identity":"436d602f-35c9-4dbc-a1cd-7cbfe44950d6","order_by":0,"name":"Zijun Wu","email":"","orcid":"","institution":"Wuhan Textile University","correspondingAuthor":false,"prefix":"","firstName":"Zijun","middleName":"","lastName":"Wu","suffix":""},{"id":549840879,"identity":"d7f9de13-a30a-4838-9c13-28efe489cf0d","order_by":1,"name":"Renbin 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Here, we assume that the topological configuration is a combination of a set of basic geometries defined by the same Non-Uniform Rational B-spline (NURBS) parameters. Within the design domain, the geometric boundary distribution position field is delineated through the utilization of mesh node and geometry boundary normal vectors. The explicit geometry mapping function is constructed by integrating the field values of all the boundaries within the geometry, which allows for the calculation of the proportion of mesh occupied by the geometries using step function. The relative density of each mesh can then be calculated. This allows the equations to be iterated and complex design results to be obtained. In the optimization process, the design variables are the geometric parameters of each individual geometry. And the Method of Moving Asymptotes (MMA) method is selected to update these design variables. 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