Sparse Sensor Placement for a Cyclonic Burner under MILD Combustion Conditions

Sparse Sensor Placement for a Cyclonic Burner under MILD Combustion Conditions | 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 Sparse Sensor Placement for a Cyclonic Burner under MILD Combustion Conditions Vincenzo Rosati, Giovanni Battista Ariemma, Giancarlo Sorrentino, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7608991/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Mar, 2026 Read the published version in Flow, Turbulence and Combustion → Version 1 posted 10 You are reading this latest preprint version Abstract This study focuses on the development of a Reduced-Order Model based on a Sparse Sensing framework to reconstruct the thermochemical state of a cyclonic burner operating under MILD combustion with \((CH_4)\) /air reactant mixtures under atmospheric pressure. A RANS-based dataset, consistent with performed experiments, is decomposed via Proper Orthogonal Decomposition and coupled with QR Column Pivoting algorithm to optimize sensor placement under practical configurations (Unconstrained, Region- and Distance-constrained, Predetermined). The operating conditions consider a nominal thermal power 6-8 kW and \((\Phi\in[0.6,1.1])\) , where 33 simulations were performed of which 30 were used for training and 3 for testing. The performance to reconstruct and predict temperature and \((X_{CO_2})\) was evaluated for all the scenarios. The approach achieves \((\approx)\) 6% median relative reconstruction error, remaining typically \((\leq)\) 10% in the most challenging cases, while reconstruction time is \((\approx)\) 0.1-0.15 s, enabling near real-time monitoring. Furthermore, a trade-off analysis between the number of retained modes and sensors, quantification of the propagation of sensor uncertainty, and the computational costs for each constraint strategy was conducted. Results support the development of soft sensors and digital twin applications for MILD combustion, with validated performance at the outlet and in the chamber mid-plane, and methodical guidance for constrained sensor layout design. MILD combustion Sparse Sensing Data-driven Machine-learning QRCP algorithm. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Mar, 2026 Read the published version in Flow, Turbulence and Combustion → Version 1 posted Editorial decision: Revision requested 26 Dec, 2025 Reviews received at journal 26 Dec, 2025 Reviewers agreed at journal 22 Dec, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers invited by journal 15 Sep, 2025 Editor assigned by journal 15 Sep, 2025 Submission checks completed at journal 14 Sep, 2025 First submitted to journal 13 Sep, 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-7608991","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515806524,"identity":"abab6501-1b44-493f-b38b-2a231b321de8","order_by":0,"name":"Vincenzo 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A RANS-based dataset, consistent with performed experiments, is decomposed via Proper Orthogonal Decomposition and coupled with QR Column Pivoting algorithm to optimize sensor placement under practical configurations (Unconstrained, Region- and Distance-constrained, Predetermined). The operating conditions consider a nominal thermal power 6-8 kW and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\Phi\\in[0.6,1.1])\\)\u003c/span\u003e\u003c/span\u003e, where 33 simulations were performed of which 30 were used for training and 3 for testing. The performance to reconstruct and predict temperature and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((X_{CO_2})\\)\u003c/span\u003e\u003c/span\u003e was evaluated for all the scenarios. The approach achieves \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\approx)\\)\u003c/span\u003e\u003c/span\u003e6% median relative reconstruction error, remaining typically \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\leq)\\)\u003c/span\u003e\u003c/span\u003e10% in the most challenging cases, while reconstruction time is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\approx)\\)\u003c/span\u003e\u003c/span\u003e0.1-0.15 s, enabling near real-time monitoring. Furthermore, a trade-off analysis between the number of retained modes and sensors, quantification of the propagation of sensor uncertainty, and the computational costs for each constraint strategy was conducted. Results support the development of soft sensors and digital twin applications for MILD combustion, with validated performance at the outlet and in the chamber mid-plane, and methodical guidance for constrained sensor layout design.\u003c/p\u003e","manuscriptTitle":"Sparse Sensor Placement for a Cyclonic Burner under MILD Combustion Conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 06:40:40","doi":"10.21203/rs.3.rs-7608991/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-26T14:55:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-26T14:46:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226237759218758246250872949686008505746","date":"2025-12-22T09:26:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254926671155929003719829656469352398659","date":"2025-10-15T15:21:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T13:53:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79518046007373031686450024625390992149","date":"2025-09-15T12:09:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-15T09:24:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-15T09:14:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-15T02:18:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Flow, Turbulence and Combustion","date":"2025-09-13T18:19:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"flow-turbulence-and-combustion","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"appl","sideBox":"Learn more about [Flow, Turbulence and Combustion](http://link.springer.com/journal/10494)","snPcode":"10494","submissionUrl":"https://submission.nature.com/new-submission/10494/3","title":"Flow, Turbulence and Combustion","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4a376223-1c62-4e4e-bcc7-097e5fae2d3f","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:04:07+00:00","versionOfRecord":{"articleIdentity":"rs-7608991","link":"https://doi.org/10.1007/s10494-026-00733-3","journal":{"identity":"flow-turbulence-and-combustion","isVorOnly":false,"title":"Flow, Turbulence and Combustion"},"publishedOn":"2026-03-09 15:58:08","publishedOnDateReadable":"March 9th, 2026"},"versionCreatedAt":"2025-09-23 06:40:40","video":"","vorDoi":"10.1007/s10494-026-00733-3","vorDoiUrl":"https://doi.org/10.1007/s10494-026-00733-3","workflowStages":[]},"version":"v1","identity":"rs-7608991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7608991","identity":"rs-7608991","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}