Optimization of micro-rotation effect on magnetohydrodynamic nanofluid flow with artificial neural network

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimization of micro-rotation effect on magnetohydrodynamic nanofluid flow with artificial neural network

Popis výsledku v původním jazyce

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 &quot;R&quot; 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.

Název v anglickém jazyce

Optimization of micro-rotation effect on magnetohydrodynamic nanofluid flow with artificial neural network

Popis výsledku anglicky

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 &quot;R&quot; 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik

ISSN

0044-2267

e-ISSN

—

Svazek periodika

104

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

19

Strana od-do

—

Kód UT WoS článku

001247876600001

EID výsledku v databázi Scopus

2-s2.0-85196005247