Neural network analysis of bioconvection effects on heat and mass transfer in Non-Newtonian chemically reactive nanofluids
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Neural network analysis of bioconvection effects on heat and mass transfer in Non-Newtonian chemically reactive nanofluids
Original language description
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'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' 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.
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
20300 - Mechanical engineering
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
Case Studies in Thermal Engineering
ISSN
2214-157X
e-ISSN
2214-157X
Volume of the periodical
64
Issue of the periodical within the volume
December
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
26
Pages from-to
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UT code for WoS article
001370785300001
EID of the result in the Scopus database
2-s2.0-85210065347