Chalak Qazani, Mohammad Reza, Aslfattahi, Navid, Kulish, Vladimir, Asadi, Houshyar, Schmirler, Michal, Said, Zafar, Afzal, Asif, Kabir, H.M. Dipu, and Arici, Müslüm (2023) An optimised deep learning method for the prediction of dynamic viscosity of MXene-based nanofluid. Journal Of The Brazilian Society Of Mechanical Sciences And Engineering, 45. 428.

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Abstract

This study designs and develops a new optimised deep learning method to calculate the dynamic viscosity using the temperature and nanoflake 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 definition 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 nanoflake 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.

Item ID:	86726
Item Type:	Article (Research - C1)
ISSN:	1806-3691
Copyright Information:	© The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023
Date Deposited:	10 Sep 2025 00:54
FoR Codes:	40 ENGINEERING > 4012 Fluid mechanics and thermal engineering > 401210 Microfluidics and nanofluidics @ 40% 46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460199 Applied computing not elsewhere classified @ 60%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 70% 17 ENERGY > 1701 Energy efficiency > 170102 Industrial energy efficiency @ 30%
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