Criteria for dynamical clustering in permanently excited granular gases: Comparison and estimation with machine learning

Criteria for dynamical clustering in permanently excited granular gases: Comparison and estimation with machine learning | 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 Criteria for dynamical clustering in permanently excited granular gases: Comparison and estimation with machine learning Sai Preetham Sata, Ralf Stannarius, Dmitry Puzyrev This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6243611/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Aug, 2025 Read the published version in Granular Matter → Version 1 posted You are reading this latest preprint version Abstract When dense granular gases in microgravity are continuously excited mechanically, spatial inhomogeneities of the particle distribution can emerge. With a sufficiently large overall packing fraction, a significant share of particles tend to concentrate in strongly overpopulated regions, the so-called clusters. This phenomenon, dynamical clustering, is caused by a complex interaction between energy influx and dissipation. The number density of particles, the geometry of the container, and the excitation strength influence cluster formation. Quantification of the clustering thresholds is not trivial. We perform Discrete Element Method (DEM) simulations for ensembles of frictional spheres in a cuboid container to generate synthetic data and apply the Kolmogorov–Smirnov test and a caging criterion to the local packing fraction profiles. Both tests yield similar thresholds in some cases but significantly diverge in others. We identify major drawbacks in these approaches and discuss more advanced statistical criteria based on dynamical properties of the ensemble.A machine learning approach that predicts dynamic clustering from known system parameters is proposed and tested. It avoids the necessity of complex numerical simulations. Granular gas Dynamical clustering Discrete Element Method Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Aug, 2025 Read the published version in Granular Matter → 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. 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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-6243611","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447557013,"identity":"e492fe50-52dd-4758-b6a6-be9d333b2f2f","order_by":0,"name":"Sai Preetham Sata","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYFACxgYwxQ/hMROtxYBBsoF4LWBgwGBwgFgt8u2HGxh/1PyRNz7eY/yah8FajrDxZxIbmHmOGRhuO3PGzJqHId2YCBcBtTCwGTBuu5GWZszDcDixgaDD+h8CHfbPwH7zDIiWeoJaGG4AjeVtM0jcIJF8+DFQSwJhh9142HCYt884ecaZw8cY5xikGxLhsPSHD398k7Ptb29s/vCmwlqeoC0gcABKs0kAQ4M0wPyBRA2jYBSMglEwQgAAlMY7CHRWHMUAAAAASUVORK5CYII=","orcid":"","institution":"Otto von Guericke University Magdeburg","correspondingAuthor":true,"prefix":"","firstName":"Sai","middleName":"Preetham","lastName":"Sata","suffix":""},{"id":447557014,"identity":"05e3ae14-3ab0-414f-92fb-34804120d5ca","order_by":1,"name":"Ralf Stannarius","email":"","orcid":"","institution":"Brandenburg University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ralf","middleName":"","lastName":"Stannarius","suffix":""},{"id":447557015,"identity":"056c8e31-b59c-4ec8-973a-def4316985f6","order_by":2,"name":"Dmitry Puzyrev","email":"","orcid":"","institution":"Otto von Guericke University Magdeburg","correspondingAuthor":false,"prefix":"","firstName":"Dmitry","middleName":"","lastName":"Puzyrev","suffix":""}],"badges":[],"createdAt":"2025-03-17 10:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6243611/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6243611/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10035-025-01560-5","type":"published","date":"2025-08-21T16:12:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89847046,"identity":"659ce11d-d4c7-4d9d-bf0a-a12103b66adb","added_by":"auto","created_at":"2025-08-25 16:39:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3350209,"visible":true,"origin":"","legend":"","description":"","filename":"CriteriafordynamicalclusteringinpermanentlyexcitedgranulargasesComparisonandestimationwithmachinelearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6243611/v1_covered_b7d4a3a4-2b24-490a-98fe-f04a38752675.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Criteria for dynamical clustering in permanently excited granular gases: Comparison and estimation with machine learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Granular gas, Dynamical clustering, Discrete Element Method","lastPublishedDoi":"10.21203/rs.3.rs-6243611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6243611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"When dense granular gases in microgravity are continuously excited mechanically, spatial inhomogeneities of the particle distribution can emerge. 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