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dc.contributor.authorLiebsch, Melvin
dc.contributor.authorRussenschuck, Stephan
dc.contributor.authorKurz, Stefan
dc.date.accessioned2023-01-02T06:10:56Z
dc.date.available2023-01-02T06:10:56Z
dc.date.issued2023
dc.identifier.citationLiebsch, M., Russenschuck, S., & Kurz, S. (2023). BEM-Based Magnetic Field Reconstruction by Ensemble Kálmán Filtering. <i>Computational Methods in Applied Mathematics</i>, <i>23</i>(2), 405-424. <a href="https://doi.org/10.1515/cmam-2022-0121" target="_blank">https://doi.org/10.1515/cmam-2022-0121</a>
dc.identifier.otherCONVID_164403935
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/84639
dc.description.abstractMagnetic fields generated by normal or superconducting electromagnets are used to guide and focus particle beams in storage rings, synchrotron light sources, mass spectrometers, and beamlines for radiotherapy. The accurate determination of the magnetic field by measurement is critical for the prediction of the particle beam trajectory and hence the design of the accelerator complex. In this context, state-of-the-art numerical field computation makes use of boundary-element methods (BEM) to express the magnetic field. This enables the accurate computation of higher-order partial derivatives and local expansions of magnetic potentials used in efficient numerical codes for particle tracking. In this paper, we present an approach to infer the boundary data of an indirect BEM formulation from magnetic field measurements by ensemble Kálmán filtering. In this way, measurement uncertainties can be propagated to the boundary data, magnetic field and potentials, and to the beam related quantities derived from particle tracking. We provide results obtained from real measurement data of a curved dipole magnet using a Hall probe mapper system.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherWalter de Gruyter GmbH
dc.relation.ispartofseriesComputational Methods in Applied Mathematics
dc.rightsCC BY 4.0
dc.subject.otherboundary element methods
dc.subject.otherparticle accelerator magnets
dc.subject.otherbayesian inference
dc.subject.otherdata assimilation
dc.subject.othermagnetic measurements
dc.titleBEM-Based Magnetic Field Reconstruction by Ensemble Kálmán Filtering
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202301021000
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange405-424
dc.relation.issn1609-4840
dc.relation.numberinseries2
dc.relation.volume23
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 the author(s)
dc.rights.accesslevelopenAccessfi
dc.subject.ysomagneettikentät
dc.subject.ysomittaus
dc.subject.ysomittauslaitteet
dc.subject.ysobayesilainen menetelmä
dc.subject.ysofysiikka
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p19032
jyx.subject.urihttp://www.yso.fi/onto/yso/p4794
jyx.subject.urihttp://www.yso.fi/onto/yso/p3583
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
jyx.subject.urihttp://www.yso.fi/onto/yso/p900
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1515/cmam-2022-0121
jyx.fundinginformationThe work of Melvin Liebsch is supported by the Graduate School CE within the Centre for Computational Engineering at Technische Universität Darmstadt.
dc.type.okmA1


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