Digital-Twin approach to predict the drag coefficient of random arrays of spheres suspended in Giesekus viscoelastic fluids

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dc.contributor.authorLoiro, Carina Luzia Melo Monteiropor
dc.contributor.authorFernandes, C.por
dc.contributor.authorMcKinley, G. H.por
dc.contributor.authorFaroughi, S. A.por
dc.date.accessioned2021-11-15T12:33:02Z
dc.date.available2021-11-15T12:33:02Z
dc.date.issued2021
dc.descriptionApresentação efetuada na 17th International Conference of Computational Methods in Sciences and Engineering (ICCMSE 2021), Greece, 2021por
dc.description.sponsorshipThe authors would like to acknowledge the funding by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT - Portuguese Foundation for Science and Technology under the projects UIDB/05256/2020 and UIDP/05256/2020 and MIT-EXPL/TDI/0038/2019 – APROVA - Deep learning for particle-laden viscoelastic flow modelling (POCI-01-0145-FEDER-016665).por
dc.distributioninternationalpor
dc.identifier.urihttps://hdl.handle.net/1822/74698
dc.language.isoengpor
dc.peerreviewednopor
dc.relationUIDB/05256/2020por
dc.relationUIDP/05256/2020por
dc.relationMIT-EXPL/TDI/0038/2019por
dc.relationPOCI-01-0145-FEDER-016665por
dc.rightsopenAccesspor
dc.subject.fosEngenharia e Tecnologia::Engenharia Mecânicapor
dc.subject.odsIndústria, inovação e infraestruturaspor
dc.titleDigital-Twin approach to predict the drag coefficient of random arrays of spheres suspended in Giesekus viscoelastic fluidspor
dc.typeoralPresentationpor
dspace.entity.typePublicationen
sdum.conferencePublication17th International Conference of Computational Methods in Sciences and Engineering (ICCMSE 2021)por

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