Thermofluidic transport of Williamson flow in stratified medium with radiative energy and heat source aspects by machine learning paradigm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F24%3A10255706" target="_blank" >RIV/61989100:27740/24:10255706 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2666202724002593#ack0001" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2666202724002593#ack0001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ijft.2024.100818" target="_blank" >10.1016/j.ijft.2024.100818</a>
Alternative languages
Result language
angličtina
Original language name
Thermofluidic transport of Williamson flow in stratified medium with radiative energy and heat source aspects by machine learning paradigm
Original language description
This study investigates Williamson fluid with stratification aspects through an inclined medium with radiative effects and with consideration of transversally applied magnetic field. Additionally, the study involves novel contribution of thermal generating source and chemically reactive species. Modelling is conceded by incorporating conservation laws in view of ordinary differential setup after employing similar variables. Afterwards, numerical simulations through shooting and Rk-4 procedures are executed to inspect the behavior of flow and thermosolutal distributions versus variation in key parameters. Subsequently, the collected data is evaluated by utilizing a multilayer perceptron-based ANN model. The input data for the heat flux, corresponding to different fluid model parameters, is trained by employing Levenberg-Marquardt paradigm and validated against numerical experiment results. The precision of the predicted data is assessed by calculating the mean squared error, determination coefficient and error rating scale. The magnitude of heat flux coefficient elevates up to 15 % in the existence of radiation effect, while depreciates up to 6 % in the presence of stratification effect. The implementation of ANN model depicts a mean square error value 1.36x10MINUS SIGN 3 when no heat source, which rises to 1.41x10MINUS SIGN 2 when a heat source is present. From small values of mean squared error for testing, training and validation estimated for Nusselt number ensures the performance of developed ANN network. (C) 2024
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
21100 - Other engineering and technologies
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
International Journal of Thermofluids
ISSN
2666-2027
e-ISSN
2666-2027
Volume of the periodical
24
Issue of the periodical within the volume
November
Country of publishing house
GB - UNITED KINGDOM
Number of pages
14
Pages from-to
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UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85202575385