Lung digital twin COVID-19 infection: a multiphysics - multiscale HPC-modeling based on CFPD and agent-based model coupled simulations

dc.contributor.authorNovell Mazzara, Alice
dc.contributor.authorMuñoz Puente, Fernando
dc.contributor.authorNtiniakou, Thaleia
dc.contributor.authorMontagud Aquino, Arnau
dc.contributor.authorHouzeaux, Guillaume
dc.contributor.authorEguzkitza, Ane Beatriz
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Matemàtica Aplicada
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2025-06-12T09:36:58Z
dc.date.available2025-06-12T09:36:58Z
dc.date.issued2024
dc.description.abstractThe present work is one of the three pieces (upper airways, lower conductive airways and respiratory zone) of a digital twin lung model developed by the Physical and Numerical Modelling research group from the CASE department in Barcelona Supercomputing Center (BSC). In particular, the study presents the solution of fluid flow and SARS-COV-2 particle transport in the lower conductive zone of the lungs, using a geometry based on patient specific images. The specific context of the current work is framed within the European Project: CREXDATA: Critical Action Planning over Extreme-Scale Data. Its general vision is to develop a generic platform for real-time critical situation management including flexible action planning and agile decision making over streaming data of extreme scale and complexity. One of the use cases of the project is the COVID-19 pandemic crisis, studying viral evolution in patients. To that end, the first step is to develop a mechanistic multiscale model to build a toolbox aimed at having a digital twin for the treatment of patients.
dc.description.peerreviewedPeer Reviewed
dc.description.versionPostprint (published version)
dc.format.extent7 p.
dc.identifier.citationSalvadó, M. [et al.]. Lung digital twin COVID-19 infection: a multiphysics - multiscale HPC-modeling based on CFPD and agent-based model coupled simulations. A: International Conference on Parallel Computational Fluid Dynamics. "Proceedings of the 35th Parallel Computational Fluid Dynamics International Conference 2024". Jülich: Forschungszentrum Jülich GmbH, 2024. ISBN 978-3-95806-819-3. DOI 10.34734/FZJ-2025-02477 .
dc.identifier.doi10.34734/FZJ-2025-02477
dc.identifier.isbn978-3-95806-819-3
dc.identifier.urihttps://hdl.handle.net/2117/431498
dc.language.isoeng
dc.publisherForschungszentrum Jülich GmbH
dc.relation.publisherversionhttps://juser.fz-juelich.de/record/1042261
dc.rights.accessOpen Access
dc.rights.licensenameAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subject.lcshCOVID-19 (Disease)
dc.subject.lemacCOVID-19 (Malaltia)
dc.subject.otherComputational fluid and particle simulations
dc.subject.otherSARS-CoV-2 transport and infection
dc.subject.otherAgent-based models
dc.subject.otherCoupled multi-physics problems
dc.subject.otherPersonalized medicine
dc.subject.otherMultiscale lung digital twin
dc.titleLung digital twin COVID-19 infection: a multiphysics - multiscale HPC-modeling based on CFPD and agent-based model coupled simulations
dc.typeConference report
dspace.entity.typePublication
local.citation.authorSalvadó, M.; Muñoz, F.; Ntiniakou, T.; Montagud, A.; Houzeaux, G.; Eguzkitza, A. B.
local.citation.contributorInternational Conference on Parallel Computational Fluid Dynamics
local.citation.publicationNameProceedings of the 35th Parallel Computational Fluid Dynamics International Conference 2024
local.citation.pubplaceJülich
local.identifier.drac42198260

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