Evaluating Machine‑Learned Nuclear Data Precision in Full‑Core Nuclear Reactor Calculations: Computational Efficiency, Criticality and Burn‑up Analysis

Evaluating Machine‑Learned Nuclear Data Precision in Full‑Core Nuclear Reactor Calculations: Computational Efficiency, Criticality and Burn‑up Analysis | 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 Evaluating Machine‑Learned Nuclear Data Precision in Full‑Core Nuclear Reactor Calculations: Computational Efficiency, Criticality and Burn‑up Analysis Alexander Hashemi, Rafael Macián-Juan, Martin Ohlerich This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7831183/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract This study evaluates the novel machine learning based reduction of cross-sections and energy grid of continuous-energy nuclear data for a one-year full-core Monte Carlo criticality and burn-up analysis using OpenMC. The approach edits OpenMC’s ENDF/B-VII.1 Hierarchical Data Format, version 5 (HDF5) nuclear data files, retaining ≈10% to 50% of nuclear data for 23 nuclides while preserving thresholds and resonances. EPR and VVER-1000 full-core models benchmark reduced nuclear data library against the original (windowed multipole disabled), to quantify performance and fidelity. Wall time decreased by 17.81% in EPR and 42.5% in VVER-1000. Peak memory (MaxRSS) decreased by 4.4% in EPR and increased by 5.0% in VVER-1000. The maximum absolute difference in k_eff(t) for VVER-1000 remains within 96.79 pcm at all times. VVER-1000 end of cycle reaction rates relative differences found for U-235 (n,f) 0.0017%, U-238 (n,f) 0.0605%, Xe-135 (n,γ) 0.0128%, Sm-149 (n,γ) 0.03%. Inventories EOC relative difference were 0.0039% U-235, 0.0003% U-238, 0.0135% Xe-135, 0.0341% Sm-149. The EOC relative difference for the Plutonium vector has been analyzed. Results prove that the developed reduction method accelerates full-core analysis, reduces MaxRSS while maintaining fidelity in neutronics studies. Physical sciences/Energy science and technology Physical sciences/Physics Nuclear Data Machine Learning OpenMC Monte Carlo Transport Full-Core Analysis Pressurized Water Reactors Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Dec, 2025 Reviews received at journal 07 Dec, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviews received at journal 06 Nov, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers invited by journal 16 Oct, 2025 Editor assigned by journal 16 Oct, 2025 Editor invited by journal 15 Oct, 2025 Submission checks completed at journal 13 Oct, 2025 First submitted to journal 13 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-7831183","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":528793488,"identity":"97977c67-8b8c-4844-9e51-391bf4f32cf4","order_by":0,"name":"Alexander Hashemi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie3RMUvEMBTA8VcK7ZLcranV9is0BM4h4hdxuSNgl3Z3u8LBc9H9Nr/GjVcenMt9AMVFEdxEHTPIWQoniOQON4f8IBkCf15CADzvP4r7PewWAwgagChuticO4a+E0V8TEGZ3MpyFK2EXOjtuePthFydng+RtdXAHOnMlgiIjr9elOlwOTMrX5zWmJkorKJVzDDH5yJEmc2BFGiBtE5o0jiKn4Xv7iTTtEmUtbmpMqE+mrqQgFjx3U8YC2EhwXNYowj4Zu+4lKVLqCEs5D9lIczQ1MqN0VZTSNSW7nb0kr6hzEV+pe4un9c1l+/RQXejc+fxvPz+i2B94nud5bl9dYkxk4wTt9wAAAABJRU5ErkJggg==","orcid":"","institution":"Technical University of Munich","correspondingAuthor":true,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Hashemi","suffix":""},{"id":528793489,"identity":"8f7dd517-23c2-4480-b994-7dd2d5723d27","order_by":1,"name":"Rafael Macián-Juan","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Macián-Juan","suffix":""},{"id":528793490,"identity":"903cebba-0533-458b-af4f-00e52db862ba","order_by":2,"name":"Martin Ohlerich","email":"","orcid":"","institution":"Bavarian Academy of Sciences and Humanities","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Ohlerich","suffix":""}],"badges":[],"createdAt":"2025-10-11 03:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7831183/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7831183/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-35227-9","type":"published","date":"2026-01-10T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100069997,"identity":"335b402b-c06d-4417-9a62-6b4f200d02bf","added_by":"auto","created_at":"2026-01-12 16:15:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18426370,"visible":true,"origin":"","legend":"","description":"","filename":"EvaluatingMachineLearnedNuclearDataPrecisioninFullCoreNuclearReactorCalculationsSecondVersion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7831183/v1_covered_6626daac-853f-49b5-8df1-a5b7dfa56ee7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Machine‑Learned Nuclear Data Precision in Full‑Core Nuclear Reactor Calculations: Computational Efficiency, Criticality and Burn‑up Analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"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},"keywords":"Nuclear Data, Machine Learning, OpenMC, Monte Carlo Transport, Full-Core Analysis, Pressurized Water Reactors","lastPublishedDoi":"10.21203/rs.3.rs-7831183/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7831183/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluates the novel machine learning based reduction of cross-sections and energy grid of continuous-energy nuclear data for a one-year full-core Monte Carlo criticality and burn-up analysis using OpenMC. The approach edits OpenMC’s ENDF/B-VII.1 Hierarchical Data Format, version 5 (HDF5) nuclear data files, retaining ≈10% to 50% of nuclear data for 23 nuclides while preserving thresholds and resonances. EPR and VVER-1000 full-core models benchmark reduced nuclear data library against the original (windowed multipole disabled), to quantify performance and fidelity. Wall time decreased by 17.81% in EPR and 42.5% in VVER-1000. Peak memory (MaxRSS) decreased by 4.4% in EPR and increased by 5.0% in VVER-1000. The maximum absolute difference in k_eff(t) for VVER-1000 remains within 96.79 pcm at all times. VVER-1000 end of cycle reaction rates relative differences found for U-235 (n,f) 0.0017%, U-238 (n,f) 0.0605%, Xe-135 (n,γ) 0.0128%, Sm-149 (n,γ) 0.03%. Inventories EOC relative difference were 0.0039% U-235, 0.0003% U-238, 0.0135% Xe-135, 0.0341% Sm-149. The EOC relative difference for the Plutonium vector has been analyzed. Results prove that the developed reduction method accelerates full-core analysis, reduces MaxRSS while maintaining fidelity in neutronics studies.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Evaluating Machine‑Learned Nuclear Data Precision in Full‑Core Nuclear Reactor Calculations: Computational Efficiency, Criticality and Burn‑up Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 03:10:53","doi":"10.21203/rs.3.rs-7831183/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-08T04:15:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-07T10:50:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113179451991518493253676889907751581598","date":"2025-11-26T12:21:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125220381109169090290917770852681866466","date":"2025-11-26T00:10:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T19:27:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179960524707460132271818863241660568087","date":"2025-10-30T13:30:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16966217604385595400706881864359886482","date":"2025-10-16T12:22:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-16T12:16:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-16T12:10:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-16T02:35:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-13T11:24:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-13T11:22:30+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":"634ce4ee-947c-4835-a0a1-07a836a95c4f","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56200403,"name":"Physical sciences/Energy science and technology"},{"id":56200404,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2026-01-12T16:07:44+00:00","versionOfRecord":{"articleIdentity":"rs-7831183","link":"https://doi.org/10.1038/s41598-026-35227-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-10 15:57:54","publishedOnDateReadable":"January 10th, 2026"},"versionCreatedAt":"2025-10-14 03:10:53","video":"","vorDoi":"10.1038/s41598-026-35227-9","vorDoiUrl":"https://doi.org/10.1038/s41598-026-35227-9","workflowStages":[]},"version":"v1","identity":"rs-7831183","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7831183","identity":"rs-7831183","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}