Neural network analysis of bioconvection effects on heat and mass transfer in Non-Newtonian chemically reactive nanofluids

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F24%3A10256384" target="_blank" >RIV/61989100:27740/24:10256384 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2214157X2401565X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2214157X2401565X?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csite.2024.105534" target="_blank" >10.1016/j.csite.2024.105534</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural network analysis of bioconvection effects on heat and mass transfer in Non-Newtonian chemically reactive nanofluids

Popis výsledku v původním jazyce

Using artificial neural networks, this study sought to investigate the magneto Williamson twophase nanofluid, taking into account chemical reactions and the motion of gyrotactic motile microorganisms. Fluid flow behavior is influenced by chemical reactions, magnetic effects, Brownian motion, and thermophoresis, according to the study. Thermal transmission is enhanced in non-Newtonian fluids as a result of their propensity to thin under shear, increased turbulence, and superior convective heat transfer. As a result of the fluid&apos;s increased thermal conductivity, the incorporation of nanoparticles enhances heat conduction. Additionally, epidermis friction, Nusselt and Sherwood numbers, and the quantity of motile microorganisms were assessed in the study. The overall Absolute Errors lies in the range of 10-2to 10-10.The mean squared error generated by Neural Networks lies in the range of 10-02 - 10-10, and 10- 02 - 10-09 respectively. Suction or injection parameter and Prandtl number have an inverse relation with fluid temperature, while Thermophoretic parameter have a direct relation. Thermophoretic parameter, Schmidt number and suction or injection parameter have an inverse relation with the concentration of nanofluid and gyrotactic microorganisms&apos; density, while micro-organisms density have a direct relation with the microorganisms. Engineering and medicine have utilized bioconvection, a process involving heat transfer and microorganism motion, in the development of nanomedicine, pharmacokinetics, drug delivery, and biosensors, among others. Solvers utilizing stochastic numerical computing include nonlinear networks, atomistic physics, thermodynamics, astrometry, fluid mechanics, nanobiology. As a result, variant scenarios are then tested, trained, and validated, in order to prove its accuracy.

Název v anglickém jazyce

Neural network analysis of bioconvection effects on heat and mass transfer in Non-Newtonian chemically reactive nanofluids

Popis výsledku anglicky

Using artificial neural networks, this study sought to investigate the magneto Williamson twophase nanofluid, taking into account chemical reactions and the motion of gyrotactic motile microorganisms. Fluid flow behavior is influenced by chemical reactions, magnetic effects, Brownian motion, and thermophoresis, according to the study. Thermal transmission is enhanced in non-Newtonian fluids as a result of their propensity to thin under shear, increased turbulence, and superior convective heat transfer. As a result of the fluid&apos;s increased thermal conductivity, the incorporation of nanoparticles enhances heat conduction. Additionally, epidermis friction, Nusselt and Sherwood numbers, and the quantity of motile microorganisms were assessed in the study. The overall Absolute Errors lies in the range of 10-2to 10-10.The mean squared error generated by Neural Networks lies in the range of 10-02 - 10-10, and 10- 02 - 10-09 respectively. Suction or injection parameter and Prandtl number have an inverse relation with fluid temperature, while Thermophoretic parameter have a direct relation. Thermophoretic parameter, Schmidt number and suction or injection parameter have an inverse relation with the concentration of nanofluid and gyrotactic microorganisms&apos; density, while micro-organisms density have a direct relation with the microorganisms. Engineering and medicine have utilized bioconvection, a process involving heat transfer and microorganism motion, in the development of nanomedicine, pharmacokinetics, drug delivery, and biosensors, among others. Solvers utilizing stochastic numerical computing include nonlinear networks, atomistic physics, thermodynamics, astrometry, fluid mechanics, nanobiology. As a result, variant scenarios are then tested, trained, and validated, in order to prove its accuracy.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20300 - Mechanical engineering

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

Case Studies in Thermal Engineering

ISSN

2214-157X

e-ISSN

2214-157X

Svazek periodika

64

Číslo periodika v rámci svazku

December

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

26

Strana od-do

—

Kód UT WoS článku

001370785300001

EID výsledku v databázi Scopus

2-s2.0-85210065347