Optimization of micro-rotation effect on magnetohydrodynamic nanofluid flow with artificial neural network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F24%3A10255610" target="_blank" >RIV/61989100:27740/24:10255610 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1002/zamm.202300498" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/zamm.202300498</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/zamm.202300498" target="_blank" >10.1002/zamm.202300498</a>
Alternative languages
Result language
angličtina
Original language name
Optimization of micro-rotation effect on magnetohydrodynamic nanofluid flow with artificial neural network
Original language description
It is a major research area in mathematics, physics, engineering, and computer science to study the heat and mass transfer properties of flow. Suspensions containing multiple nanoparticles or nanocomposites have recently gained a wide range of applications in biological research and clinical trials under certain conditions. Nanofluids are important suspensions that allow nanoparticles to disseminate and behave in a homogeneous and stable environment. Therefore, here magnetohydrodynamic micropolar nanofluid flow towards the stretching surface with artificial neural network has been considered. In this study, radiation and heat source phenomena have been presented in heat convection. Brownian and thermophoresis effects and micro-rotational particles are also taking into account. The non-linear simplified equations have been calculated numerically via Runge-Kutta fourth-order shooting process. The calculation of the Sherwood number, Nusselt number, couple stress coefficient, and skin friction coefficient has been conducted utilizing diverse parameters. Furthermore, the outcomes have been employed to create four distinct artificial neural networks. Our observation indicates that an increase in the heat source quantity (Formula presented.) leads to a rise in heat generation, resulting in a greater total heat output and an increase in the temperature field. Coefficient of determination "R" values higher than 0.99 have been obtained for the artificial neural network models. The obtained findings have shown that artificial neural networks can predict thermal parameters with high accuracy. (C) 2024 Wiley-VCH GmbH.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
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Continuities
O - Projekt operacniho programu
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik
ISSN
0044-2267
e-ISSN
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Volume of the periodical
104
Issue of the periodical within the volume
8
Country of publishing house
DE - GERMANY
Number of pages
19
Pages from-to
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UT code for WoS article
001247876600001
EID of the result in the Scopus database
2-s2.0-85196005247