Non-Periodic RVEs for Composite Modeling via Simplified Random Sequential Expansion

Non-Periodic RVEs for Composite Modeling via Simplified Random Sequential Expansion | 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 Non-Periodic RVEs for Composite Modeling via Simplified Random Sequential Expansion Fajwa Kamar Shah, Norwahida Yusoff, Sarah Kamaludin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7559970/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 Realistic digital microstructures are increasingly recognized as essential for predicting the mechanical behavior of fiber-reinforced composites within multiscale modeling frameworks. This study presents a computationally efficient Simplified Random Sequential Expansion (sRSE) algorithm for generating threedimensional, statistically random, non-periodic representative volume elements (RVEs) with non-overlapping fiber distributions. The sRSE simplifies the original RSE framework while enhancing its performance, introducing randomized reference fiber selection and spatial hashing for efficient, statistically robust microstructure generation. These enhancements enable rapid generation of digital microstructures that preserve statistical authenticity across a wide range of fiber volume fractions, achieving up to 68% in the present implementation. Validation against experimental benchmarks—using nearest-neighbor distributions, Ripley’s K function, and pair correlation functions—confirms that sRSE produces non-periodic fiber arrangements that exhibit true spatial randomness. When integrated into a finite element workflow with kinematic (symmetric) boundary conditions, the generated microstructures predict transverse elastic properties of glass-fiber/epoxy composites within 3% of experimental results. The proposed approach offers a robust and scalable pathway for producing non-periodic, simulation-ready microstructures, with direct applications in virtual testing, multiphase material design, and machine learning–driven property prediction. digital microstructure generation non-periodic RVE random fiber distribution simplified random sequential expansion multiscale modeling finite element analysis composite materials statistical microstructure validation Full Text Additional Declarations No competing interests reported. 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. 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-7559970","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515796853,"identity":"f221154a-1a8b-47a1-8eca-a63d17781244","order_by":0,"name":"Fajwa Kamar Shah","email":"","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Fajwa","middleName":"Kamar","lastName":"Shah","suffix":""},{"id":515796854,"identity":"c0d3a829-660b-4e66-9c9d-edb3d27d815e","order_by":1,"name":"Norwahida Yusoff","email":"","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Norwahida","middleName":"","lastName":"Yusoff","suffix":""},{"id":515796855,"identity":"a0738f60-a919-4d03-9555-977572d57714","order_by":2,"name":"Sarah Kamaludin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYJACZoYDQJKdgUEigcGGFC3MYC1ppGphYDhMWLl8+9mHnwvO2EXzN/MY3ni443zi/GkHGD98zMGtxeBMurH0jBvJuTMO8xhbJJ65nbjhdgKz5MxteLQAXS/N84E5t+Ewj5lEYhtQi3QCGzMvHi3y/c+Yf/N8qM+dD9FyLnH+bAJaGG6ksUnz3DicuwGi5UBiw20CWgxuPGOz5jlzPHfjYbZii8S2ZOMNtxOb8fpFvj+N+TbPsercecebN9782WYnO3928sEPH/E5DAtgbCBN/SgYBaNgFIwCDAAAUWtUjJFoiLkAAAAASUVORK5CYII=","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Kamaludin","suffix":""}],"badges":[],"createdAt":"2025-09-08 04:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7559970/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7559970/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94144226,"identity":"2552f35b-5c1a-4d6c-9840-f16f85c679da","added_by":"auto","created_at":"2025-10-22 20:46:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3234206,"visible":true,"origin":"","legend":"","description":"","filename":"NonPeriodicRVEssRSE2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7559970/v1_covered_96c68bc2-9e23-4d76-a136-a3ef9fdf2f6f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-Periodic RVEs for Composite Modeling via Simplified Random Sequential Expansion","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"digital microstructure generation, non-periodic RVE, random fiber distribution, simplified random sequential expansion, multiscale modeling, finite element analysis, composite materials, statistical microstructure validation","lastPublishedDoi":"10.21203/rs.3.rs-7559970/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7559970/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRealistic digital microstructures are increasingly recognized as essential for predicting the mechanical behavior of fiber-reinforced composites within multiscale modeling frameworks. This study presents a computationally efficient Simplified Random Sequential Expansion (sRSE) algorithm for generating threedimensional, statistically random, non-periodic representative volume elements (RVEs) with non-overlapping fiber distributions. The sRSE simplifies the original RSE framework while enhancing its performance, introducing randomized reference fiber selection and spatial hashing for efficient, statistically robust microstructure generation. These enhancements enable rapid generation of digital microstructures that preserve statistical authenticity across a wide range of fiber volume fractions, achieving up to 68% in the present implementation. Validation against experimental benchmarks—using nearest-neighbor distributions, Ripley’s K function, and pair correlation functions—confirms that sRSE produces non-periodic fiber arrangements that exhibit true spatial randomness. When integrated into a finite element workflow with kinematic (symmetric) boundary conditions, the generated microstructures predict transverse elastic properties of glass-fiber/epoxy composites within 3% of experimental results. 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