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dc.contributor.authorYang, Haoyu
dc.contributor.authorKe, Yan
dc.contributor.authorZhang, Duo
dc.date.accessioned2021-03-10T18:05:50Z
dc.date.available2021-03-10T18:05:50Z
dc.date.issued2021-03
dc.identifier.issn1755-1307
dc.identifier.otherees_680_1_012080
dc.identifier.othere6801080
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/318629
dc.description.abstractAbstract: Using machine learning (ML) method to predict permeability of porous media has shown great potential in recent years. A current problem is the lack of effective models to account for highly porous media with dilated pores. This study includes (1) generation of media (porosity = 0.8) via a Boolean process, (2) the pore size distribution (PSD) control by using different groups of homogeneous packed spherical particles (3) PSD data obtainment using the spherical contact distribution model (4) computation of the permeability via LBM simulations, (4) training of artificial neuron network (ANN) and (5) analysis of the model. It is found that the PSD could outperform the previous geometry descriptors as an input of ML framework to deal with highly porous structures with different fractions of dilated pores, however there is still room for precision enhancement.
dc.languageen
dc.publisherIOP Publishing
dc.rightsAttribution 3.0 Unported (CC BY 3.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/en
dc.subjectPaper
dc.titleA ML framework to predict permeability of highly porous media based on PSD
dc.typeArticle
dc.date.updated2021-03-10T18:05:49Z
prism.issueIdentifier1
prism.publicationNameIOP Conference Series: Earth and Environmental Science
prism.volume680
dc.identifier.doi10.17863/CAM.65745
rioxxterms.versionofrecord10.1088/1755-1315/680/1/012080
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/3.0/
dc.identifier.eissn1755-1315


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Attribution 3.0 Unported (CC BY 3.0)
Except where otherwise noted, this item's licence is described as Attribution 3.0 Unported (CC BY 3.0)