Optimization of Bioconvective Magnetized Walter's B Nanofluid Flow towards a Cylindrical Disk with Artificial Neural Networks

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dc.authoridShafiq, Anum/0000-0001-7186-7216
dc.authoridSindhu, Tabassum/0000-0001-9433-4981
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.contributor.authorShafiq, Anum
dc.contributor.authorColak, Andac Batur
dc.contributor.authorSindhu, Tabassum Naz
dc.date.accessioned2024-11-07T13:32:39Z
dc.date.available2024-11-07T13:32:39Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractNanotechnology is a fundamental component of modern technology. Researchers have concentrated their efforts in recent years on inventing various algorithms to increase heat transmission rates. Using nanoparticles in host fluids to dramatically improve the thermal properties of ordinary fluids is one way to address this problem. The article deals with the bio-convective Walter's B nanofluid with thermophoresis and Brownian diffusion through a cylindrical disk under artificial neural networks (ANNs). In addition, the thermal conductivity, radiation, and motile density of microorganisms are taken into consideration. The Buongiorno model is utilized to investigate the properties of nanofluids in motile microorganisms. By using appropriate similarity variables, a dimensionless system of a differential system is attained. The non-linear simplified system of equations has been numerically calculated via the Runge-Kutta fourth-order shooting process. The consequences of flow parameters on the velocity field, temperature distribution, species volumetric concentration, and microorganism fields are all addressed. Two distinct artificial neural network models were produced using numerical data, and their prediction performance was thoroughly examined. It is noted that according to the error histograms, the ANN model's training phase has very little error. Furthermore, mean square error values calculated for local Nusselt number, local Sherwood number, and local motile density number, parameters were obtained as 3.58x10-3, 1.24x10-3, and 3.55x10-5, respectively. Both artificial neural network models can predict with high accuracy, according to the findings of the calculated performance parameters.
dc.identifier.doi10.3390/lubricants10090209
dc.identifier.issn2075-4442
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85138670292
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/lubricants10090209
dc.identifier.urihttps://hdl.handle.net/11480/15544
dc.identifier.volume10
dc.identifier.wosWOS:000859628900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofLubricants
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectmotile microorganisms
dc.subjectWalter's B nanofluid
dc.subjectvariable thermal conductivity
dc.subjectBrownian motion
dc.subjectthermophoresis
dc.subjectartificial neural network
dc.titleOptimization of Bioconvective Magnetized Walter's B Nanofluid Flow towards a Cylindrical Disk with Artificial Neural Networks
dc.typeArticle

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