From images to physics: Multiphysics modelling of random metallic meshes

From images to physics: Multiphysics modelling of random metallic meshes | 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 From images to physics: Multiphysics modelling of random metallic meshes Dimitrios Charaklias, Dayuan Qiang, Robert Dorey, Iman Mohagheghian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8902707/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Fractal and random conductive networks are increasingly exploited in transparent heaters and smart multifunctional structures due to their inherent scalability, robustness, and efficient transport properties, yet their intrinsic disorder poses major challenges for quantitative modelling. Here we introduce a computationally efficient, image-driven framework for the automated digitisation, reconstruction, and multi-physics analysis of random conductive meshes. High-resolution microscopy images are converted into graph-based network representations via skeletonization, branch tracking, and connectivity refinement, preserving local geometry while drastically reducing degrees of freedom. An analytical model is developed to predict sheet resistance directly from extracted network metrics, enabling rapid, non-destructive electrical characterisation. The digitised networks are further used to perform coupled electro-thermal and electro-thermo-mechanical finite-element simulations. Experimental validation using silver mesh heaters embedded in polymer laminates shows excellent agreement with predicted electrical resistance, temperature evolution, and stiffness modulation under electrical loading. The framework reduces analysis time and enables systematic assessment of structural features, such as dangling branches, on device performance. This approach provides a scalable route for predictive design and optimisation of fractal-based multifunctional electronic materials. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Mathematics and computing Physical sciences/Physics Fractal networks Automated feature extraction Multi-physics modelling Rapid imaged-based non-destructive testing Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviews received at journal 13 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers invited by journal 04 Mar, 2026 Editor invited by journal 02 Mar, 2026 Editor assigned by journal 20 Feb, 2026 Submission checks completed at journal 20 Feb, 2026 First submitted to journal 17 Feb, 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. 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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-8902707","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":602054701,"identity":"262bde72-44ed-4ebb-aeff-55f74a5ce9b1","order_by":0,"name":"Dimitrios Charaklias","email":"","orcid":"","institution":"University of Surrey","correspondingAuthor":false,"prefix":"","firstName":"Dimitrios","middleName":"","lastName":"Charaklias","suffix":""},{"id":602054702,"identity":"b50e89cc-8122-4f13-b8b6-747092cfb5cf","order_by":1,"name":"Dayuan Qiang","email":"","orcid":"","institution":"University of 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