Please use this identifier to cite or link to this item: http://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4453
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dc.contributor.authorMakauskas, P.-
dc.contributor.authorPal, M.-
dc.contributor.authorKulkarni, V.-
dc.contributor.authorKashyap, A.S.-
dc.contributor.authorTyagi, H.-
dc.date.accessioned2024-05-11T14:45:57Z-
dc.date.available2024-05-11T14:45:57Z-
dc.date.issued2024-05-11-
dc.identifier.urihttp://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4453-
dc.description.abstractA neural solution methodology, using a feed-forward and a convolutional neural networks, is presented for general tensor elliptic pressure equation with discontinuous coefficients. The methodology is applicable for solving single-phase flow in porous medium, which is traditionally solved using numerical schemes like finite-volume methods. The neural solution to elliptic pressure equation is based on machine learning algorithms and could serve as a more effective alternative to finite volume schemes like two-point or multi-point discretization schemes (TPFA or MPFA) for faster and more accurate solution of elliptic pressure equation. Series of 1D and 2D test cases, where the results of Neural solutions are compared to numerical solutions obtained using two-point schemes with range of heterogeneities, are also presented to demonstrate general applicability and accuracy of the Neural solution method.en_US
dc.language.isoen_USen_US
dc.subjectNeural PDEen_US
dc.subjectElliptic equationen_US
dc.subjectHeterogeneousen_US
dc.subjectCNNen_US
dc.subjectfinite-volume methoden_US
dc.titleComparative study of modelling flows in porous media for engineering applications using finite volume and artificial neural network methodsen_US
dc.typeArticleen_US
Appears in Collections:Year-2023

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