Pressure Drop Estimation of Two-Phase Adiabatic Flows in Smooth Tubes: Development of Machine Learning-Based Pipelines

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datacite.relatedItem.firstPage181
datacite.relatedItem.issue8
datacite.relatedItem.relatedIdentifierTypeISSN
datacite.relatedItem.relatedItemIdentifier2311-5521
datacite.relatedItem.relationTypeIsPublishedIn
datacite.relatedItem.titleFluids
datacite.relatedItem.volume9
dc.contributor.authorFarshad Bolourchifard
dc.contributor.authorKeivan Ardam
dc.contributor.authorFarzad Dadras Javan
dc.contributor.authorBehzad Najafi
dc.contributor.authorPaloma Vega Penichet Domecq
dc.contributor.authorFabio Rinaldi
dc.contributor.authorLuigi Pietro Maria Colombo
dc.date.accessioned2025-04-16T12:45:17Z
dc.date.available2025-04-16T12:45:17Z
dc.date.issued2024
dc.identifier.doihttps://doi.org/10.3390/fluids9080181
dc.identifier.otherjz000104-0043
dc.identifier.urihttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/18308
dc.publisherMDPI AG
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc530
dc.titlePressure Drop Estimation of Two-Phase Adiabatic Flows in Smooth Tubes: Development of Machine Learning-Based Pipelines
dc.typeArticle
dcat.distribution.pdfhttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/18309
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