Neural network study of gyrotactic microorganism dynamics in nanofluid through porous stretched surface

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F25%3A10258819" target="_blank" >RIV/61989100:27740/25:10258819 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2590123025039106?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590123025039106?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rineng.2025.107858" target="_blank" >10.1016/j.rineng.2025.107858</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural network study of gyrotactic microorganism dynamics in nanofluid through porous stretched surface

Popis výsledku v původním jazyce

In this article, the effects of swimming gyrotactic microorganisms on magnetohydrodynamic nanofluid flow through a porous medium governed by Darcy-Forchheimer law are investigated. The nonlinear coupled mathematical model is solved using a hybrid algorithm that combines MATLAB&apos;s bvp4c solver with the Levenberg-Marquardt technique (LMT). The proposed LMT-based neural network model exhibits excellent agreement with the reference solutions, achieving extremely low mean squared error (MSE) values of 10-10, confirming its robustness and efficient convergence. The reliability of the proposed hybrid methodology is confirmed through excellent agreement between neural network predictions and numerical solutions. Parametric studies indicate that the velocity profile F &apos;(eta) enhances with higher motile microorganism parameter (Nr) and bioconvection Rayleigh number (Rb), but declines with increasing Darcy permeability parameter (beta D) and magnetic parameter M. Temperature theta(eta) rises with thermophoresis (Nt), Brownian motion (Nb), Eckert number (Ec) and magnetic parameter M, while decreasing for larger Prandtl number (Pr). The nanoparticle concentration chi(eta) is diminished for higher Schmidt (Sc) and Brownian motion (Nb) parameters, whereas it is augmented under stronger thermophoretic effects (Nt). Similarly, the motile microorganism profile chi(eta) is suppressed by larger microorganism Schmidt number (Scm), P &amp; eacute;clet number (Pe), and microorganism concentration difference ratio (Omega d), indicating reduced motile microorganism density under these conditions. These results demonstrate the effectiveness of the hybrid bvp4c-LMT framework in accurately capturing complex nonlinear transport and bioconvective dynamics in gyrotactic nanofluids.

Název v anglickém jazyce

Neural network study of gyrotactic microorganism dynamics in nanofluid through porous stretched surface

Popis výsledku anglicky

In this article, the effects of swimming gyrotactic microorganisms on magnetohydrodynamic nanofluid flow through a porous medium governed by Darcy-Forchheimer law are investigated. The nonlinear coupled mathematical model is solved using a hybrid algorithm that combines MATLAB&apos;s bvp4c solver with the Levenberg-Marquardt technique (LMT). The proposed LMT-based neural network model exhibits excellent agreement with the reference solutions, achieving extremely low mean squared error (MSE) values of 10-10, confirming its robustness and efficient convergence. The reliability of the proposed hybrid methodology is confirmed through excellent agreement between neural network predictions and numerical solutions. Parametric studies indicate that the velocity profile F &apos;(eta) enhances with higher motile microorganism parameter (Nr) and bioconvection Rayleigh number (Rb), but declines with increasing Darcy permeability parameter (beta D) and magnetic parameter M. Temperature theta(eta) rises with thermophoresis (Nt), Brownian motion (Nb), Eckert number (Ec) and magnetic parameter M, while decreasing for larger Prandtl number (Pr). The nanoparticle concentration chi(eta) is diminished for higher Schmidt (Sc) and Brownian motion (Nb) parameters, whereas it is augmented under stronger thermophoretic effects (Nt). Similarly, the motile microorganism profile chi(eta) is suppressed by larger microorganism Schmidt number (Scm), P &amp; eacute;clet number (Pe), and microorganism concentration difference ratio (Omega d), indicating reduced motile microorganism density under these conditions. These results demonstrate the effectiveness of the hybrid bvp4c-LMT framework in accurately capturing complex nonlinear transport and bioconvective dynamics in gyrotactic nanofluids.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

21100 - Other engineering and technologies

Projekt

<a href="/cs/project/EH23_021%2F0008759" target="_blank" >EH23_021/0008759: Zvýšení odolnosti energetických sítí v kontextu dekarbonizace, decentralizace a udržitelného socioekonomického rozvoje</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Results in Engineering

ISSN

2590-1230

e-ISSN

2590-1230

Svazek periodika

28

Číslo periodika v rámci svazku

December

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

22

Strana od-do

107858

Kód UT WoS článku

001607629700005

EID výsledku v databázi Scopus

2-s2.0-105020859160