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dc.contributor.authorSafiullin, N.en
dc.contributor.authorKleeorin, N.en
dc.contributor.authorPorshnev, S.en
dc.contributor.authorRogachevskii, I.en
dc.contributor.authorRuzmaikin, A.en
dc.date.accessioned2021-08-31T15:00:21Z-
dc.date.available2021-08-31T15:00:21Z-
dc.date.issued2018-
dc.identifier.citationNonlinear mean-field dynamo and prediction of solar activity / N. Safiullin, N. Kleeorin, S. Porshnev, et al. — DOI 10.1017/S0022377818000600 // Journal of Plasma Physics. — 2018. — Vol. 84. — Iss. 3. — 735840306.en
dc.identifier.issn223778-
dc.identifier.otherFinal2
dc.identifier.otherAll Open Access, Green3
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85059455239&doi=10.1017%2fS0022377818000600&partnerID=40&md5=585968d47631ba3d034386bd38a3f4e2
dc.identifier.otherhttp://arxiv.org/pdf/1712.07501m
dc.identifier.urihttp://elar.urfu.ru/handle/10995/101882-
dc.description.abstractWe apply a nonlinear mean-field dynamo model which includes a budget equation for the dynamics of Wolf numbers to predict solar activity. This dynamo model takes into account the algebraic and dynamic nonlinearities of the α effect, where the equation for the dynamic nonlinearity is derived from the conservation law for the magnetic helicity. The budget equation for the evolution of the Wolf number is based on a formation mechanism of sunspots related to the negative effective magnetic pressure instability. This instability redistributes the magnetic flux produced by the mean-field dynamo. To predict solar activity on the time scale of one month we use a method based on a combination of the numerical solution of the nonlinear mean-field dynamo equations and the artificial neural network. A comparison of the results of the prediction of the solar activity with the observed Wolf numbers demonstrates a good agreement between the forecast and observations. © 2018 Cambridge University Press. All rights reserved.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherCambridge University Pressen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceJ Plasma Phys2
dc.sourceJournal of Plasma Physicsen
dc.subjectASTROPHYSICAL PLASMASen
dc.subjectPLASMA NONLINEAR PHENOMENAen
dc.subjectSPACE PLASMA PHYSICSen
dc.subjectBUDGET CONTROLen
dc.subjectCONTROL NONLINEARITIESen
dc.subjectDYNAMICSen
dc.subjectFORECASTINGen
dc.subjectINTERACTIVE DEVICESen
dc.subjectLAWS AND LEGISLATIONen
dc.subjectMAGNETISMen
dc.subjectNEURAL NETWORKSen
dc.subjectNUMERICAL METHODSen
dc.subjectSOLAR ENERGYen
dc.subjectSOLAR RADIATIONen
dc.subjectASTROPHYSICAL PLASMAen
dc.subjectDYNAMIC NON LINEARITIESen
dc.subjectFORMATION MECHANISMen
dc.subjectMAGNETIC HELICITYen
dc.subjectNON-LINEAR PHENOMENAen
dc.subjectNUMERICAL SOLUTIONen
dc.subjectPREDICTION OF SOLAR ACTIVITYen
dc.subjectSPACE PLASMA PHYSICSen
dc.subjectNONLINEAR EQUATIONSen
dc.titleNonlinear mean-field dynamo and prediction of solar activityen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.1017/S0022377818000600-
dc.identifier.scopus85059455239-
local.contributor.employeeSafiullin, N., Department of Information Technology and Automation, Ural Federal University, 19 Mira str., Ekaterinburg, 620002, Russian Federation
local.contributor.employeeKleeorin, N., Department of Mechanical Engineering, Ben-Gurion University of the Negev, P. O. Box 653, Beer-Sheva, 84105, Israel, Nordita, KTH Royal Institute of Technology, Stockholm University, Roslagstullsbacken 23, Stockholm, 10691, Sweden
local.contributor.employeePorshnev, S., Department of Information Technology and Automation, Ural Federal University, 19 Mira str., Ekaterinburg, 620002, Russian Federation
local.contributor.employeeRogachevskii, I., Department of Mechanical Engineering, Ben-Gurion University of the Negev, P. O. Box 653, Beer-Sheva, 84105, Israel, Nordita, KTH Royal Institute of Technology, Stockholm University, Roslagstullsbacken 23, Stockholm, 10691, Sweden
local.contributor.employeeRuzmaikin, A., Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
local.issue3-
local.volume84-
dc.identifier.wos000449834900001-
local.contributor.departmentDepartment of Information Technology and Automation, Ural Federal University, 19 Mira str., Ekaterinburg, 620002, Russian Federation
local.contributor.departmentDepartment of Mechanical Engineering, Ben-Gurion University of the Negev, P. O. Box 653, Beer-Sheva, 84105, Israel
local.contributor.departmentNordita, KTH Royal Institute of Technology, Stockholm University, Roslagstullsbacken 23, Stockholm, 10691, Sweden
local.contributor.departmentJet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
local.identifier.pure8339140-
local.description.order735840306-
local.identifier.eid2-s2.0-85059455239-
local.identifier.wosWOS:000449834900001-
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