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

Qazani M. R. C., Aslfattahi N., Kulish V., Asadi H., Schmirler M., Said Z., ...Daha Fazla

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING, cilt.45, sa.8, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 45 Sayı: 8
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s40430-023-04284-w
  • Dergi Adı: JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Anahtar Kelimeler: Bayesian optimisation, Deep learning, Dynamic viscosity, Long short-term memory, Multilayer perceptron, MXene
  • Kocaeli Üniversitesi Adresli: Evet

Özet

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.