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

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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.

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Funding

This study was self-funded.

Author information

Authors and Affiliations

  1. Faculty of Computing and Information Technology (FoCIT), Sohar University, Al Jamiah Street, Sohar, 311, Oman

    Mohammad Reza Chalak Qazani

  2. Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC, 3216, Australia

    Mohammad Reza Chalak Qazani, Houshyar Asadi & H. M. Dipu Kabir

  3. Department of Fluid Mechanics and Thermodynamics, Faculty of Mechanical Engineering, Czech Technical University in Prague, Technická 4, 166 07, Prague, Czech Republic

    Navid Aslfattahi, Vladimir Kulish & Michal Schmirler

  4. Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, 517325, India

    Vladimir Kulish

  5. Sustainable and Renewable Energy Engineering Department, University of Sharjah, P.O. Box, 27272, Sharjah, United Arab Emirates

    Zafar Said

  6. U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Zafar Said

  7. Department of Mechanical Engineering, P. A. College of Engineering (Affiliated to Visvesvaraya Technological University, Belagavi),, Mangaluru, 574153, India

    Asif Afzal

  8. Department of Mechanical Engineering, Faculty of Engineering, Kocaeli University, Kocaeli, 41001, Turkey

    Müslüm Arıcı

  9. Department of Industrial and Mechanical Engineering, Lebanese American University (LAU), Byblos, Lebanon

    Zafar Said

Authors
  1. Mohammad Reza Chalak Qazani
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  2. Navid Aslfattahi
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  3. Vladimir Kulish
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  4. Houshyar Asadi
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  5. Michal Schmirler
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  6. Zafar Said
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  7. Asif Afzal
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  8. H. M. Dipu Kabir
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  9. Müslüm Arıcı
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Contributions

MRCQ performed conceptualisation, methodology, software, validation, formal analysis, investigation, data curation, writing–original draft, writing-review & editing, visualisation, supervision, and project administration; NA did conceptualisation, methodology, software, validation, formal analysis, investigation, data curation, writing–original draft, writing-review & editing, visualization, supervision, and project administration; VK gave writing– review & editing; houshyar asadi provided writing–review & editing, validation, and formal analysis; MS done writing–review & editing; ZS done writing–review & editing, supervision, and project administration; DK contributed writing–review & editing; MA did writing–review & editing.

Corresponding authors

Correspondence to Mohammad Reza Chalak Qazani, Navid Aslfattahi or Zafar Said.

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Technical Editor: Ahmad Arabkoohsar.

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Qazani, M.R.C., Aslfattahi, N., Kulish, V. et al. An optimised deep learning method for the prediction of dynamic viscosity of MXene-based nanofluid. J Braz. Soc. Mech. Sci. Eng. 45, 428 (2023). https://doi.org/10.1007/s40430-023-04284-w

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  • DOI: https://doi.org/10.1007/s40430-023-04284-w

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