Innovative thermal management in the presence of ferromagnetic hybrid nanoparticles

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F24%3A10255811" target="_blank" >RIV/61989100:27740/24:10255811 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-024-68830-9" target="_blank" >https://www.nature.com/articles/s41598-024-68830-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-024-68830-9" target="_blank" >10.1038/s41598-024-68830-9</a>

Result language

angličtina

Original language name

Innovative thermal management in the presence of ferromagnetic hybrid nanoparticles

Original language description

In the present work, a simple intelligence-based computation of artificial neural networks with the Levenberg-Marquardt backpropagation algorithm is developed to analyze the new ferromagnetic hybrid nanofluid flow model in the presence of a magnetic dipole within the context of flow over a stretching sheet. A combination of cobalt and iron (III) oxide (Co-Fe2O3) is strategically selected as ferromagnetic hybrid nanoparticles within the base fluid, water. The initial representation of the developed ferromagnetic hybrid nanofluid flow model, which is a system of highly nonlinear partial differential equations, is transformed into a system of nonlinear ordinary differential equations using appropriate similarity transformations. The reference data set of the possible outcomes is obtained from bvp4c for varying the parameters of the ferromagnetic hybrid nanofluid flow model. The estimated solutions of the proposed model are described during the testing, training, and validation phases of the backpropagated neural network. The performance evaluation and comparative study of the algorithm are carried out by regression analysis, error histograms, function fitting graphs, and mean squared error results. The findings of our study analyze the increasing effect of the ferrohydrodynamic interaction parameter β to enhance the temperature and velocity profiles, while increasing the thermal relaxation parameter α decreases the temperature profile. The performance on MSE was shown for the temperature and velocity profiles of the developed model about 9.1703eMINUS SIGN 10, 7.1313eeMINUS SIGN 10, 3.1462eMINUS SIGN 10, and 4.8747eMINUS SIGN 10. The accuracy of the artificial neural networks with the Levenberg-Marquardt algorithm method is confirmed through various analyses and comparative results with the reference data. The purpose of this study is to enhance understanding of ferromagnetic hybrid nanofluid flow models using artificial neural networks with the Levenberg-Marquardt algorithm, offering precise analysis of key parameter effects on temperature and velocity profiles. Future studies will provide novel soft computing methods that leverage artificial neural networks to effectively solve problems in fluid mechanics and expand to engineering applications, improving their usefulness in tackling real-world problems. (C) The Author(s) 2024.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

21100 - Other engineering and technologies

Project

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Continuities

O - Projekt operacniho programu

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Volume of the periodical

14

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

18

Pages from-to

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UT code for WoS article

001285457700072

EID of the result in the Scopus database

2-s2.0-85200499565