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Computational modeling of transport in porous media using an adaptive network-based fuzzy inference system

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dc.contributor.author Babanezhad, Meisam
dc.contributor.author Behroyan, Iman
dc.contributor.author Nakhjiri, Ali Taghvaie
dc.contributor.author Marjani, Azam
dc.contributor.author Shirazian, Saeed
dc.date.accessioned 2020-11-30T08:48:51Z
dc.date.available 2020-11-30T08:48:51Z
dc.date.issued 2020
dc.identifier.uri http://hdl.handle.net/10344/9491
dc.description peer-reviewed en_US
dc.description.abstract This investigation is conducted to study the integration of the artificial intelligence (AI) method with computational fluid dynamics (CFD). The case study is hydrodynamic and heat-transfer analyses of water flow in a metal foam tube under a constant wall heat flux (i.e., 55 kW/m2 ). The adaptive network-based fuzzy inference system (ANFIS) is an AI method. A 3D CFD model is established in ANSYS-FLUENT software. The velocity of the fluid in the x-direction (Ux) is considered as an output of the ANFIS. The x, y, and z coordinates of the node’s location are added to the ANFIS step-by-step to achieve the best intelligence. The number and type of membership functions (MFs) are changed in each step. The training process is done by the CFD results on the tube cross-sections at different lengths (i.e., z = 0.1, 0.2, 0.3, 0.4, 0.6, 0.7, 0.8, and 0.9), while all data (including z = 0.5) are selected for the testing process. The results showed that the ANFIS reaches the best intelligence with all three inputs, five MFs, and “gbellmf”-type MF. At this condition, the regression number is close to 1. en_US
dc.language.iso eng en_US
dc.publisher American Chemical Society en_US
dc.relation.ispartofseries ACS Omega;
dc.subject ANNs en_US
dc.subject fluid dynamics en_US
dc.subject ANFIS en_US
dc.title Computational modeling of transport in porous media using an adaptive network-based fuzzy inference system en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1021/acsomega.0c04497
dc.contributor.sponsor Ministry of Science and Higher Education of Russia en_US
dc.relation.projectid FENU-2020-0019 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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