An optimised deep learning method for the prediction of dynamic viscosity of MXene-based nanofuid

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F23%3A00367068" target="_blank" >RIV/68407700:21220/23:00367068 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s40430-023-04284-w" target="_blank" >https://doi.org/10.1007/s40430-023-04284-w</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s40430-023-04284-w" target="_blank" >10.1007/s40430-023-04284-w</a>

Result language

angličtina

Original language name

An optimised deep learning method for the prediction of dynamic viscosity of MXene-based nanofuid

Original language description

This study designs and develops a new optimised deep learning method to calculate the dynamic viscosity using the temperature and nanofake concentration. Long short-term memory (LSTM) has been a candidate as the most suitable deep learning method with the ability to reach higher accurate results with a defnition of the dropout layers during the training process to prevent the overshoot issue of the networks. In addition, the Bayesian optimisation technique is employed to extract the optimal hyperparameters of the developed LSTM to reach the system’s highest performance in predicting the dynamical viscosity based on temperature and nanofake concentration. The newly proposed method is designed and developed in MATLAB software using 80% and 20% of the dataset for training and testing of the model. The newly proposed optimised LSTM is compared with the recently developed model using multilayer perceptron (MLP) to prove the higher efficiency of our proposed technique. It should be noted that mean-squared error and root-mean-square error using the newly proposed optimised LSTM reduce by 12.56 and 3.54 times compared to the recently developed MLP model. Also, the R-square of the newly proposed optimised LSTM increases by 4.43% compared to the recently developed MLP model.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20303 - Thermodynamics

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Journal of the Brazilian Society of Mechanical Sciences and Engineering

ISSN

1678-5878

e-ISSN

1806-3691

Volume of the periodical

2023 (45)

Issue of the periodical within the volume

07

Country of publishing house

DE - GERMANY

Number of pages

14

Pages from-to

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UT code for WoS article

001045098600003

EID of the result in the Scopus database

2-s2.0-85165324126