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Using of Artificial Neural Networks (ANNs) to predict the rheological behavior of MgO-Water nanofluid in a different volume fraction of nanoparticles, temperatures, and shear rates
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  • Yicheng Li,
  • Rasool Kalbasi,
  • Arash Karimipour,
  • M. Sharifpur,
  • Josua Petrus Meyer
Yicheng Li
Jiangsu University
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Rasool Kalbasi
Islamic Azad University Najafabad Branch
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Arash Karimipour
Ton Duc Thang University
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M. Sharifpur
University of Pretoria
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Josua Petrus Meyer
University of Pretoria
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Abstract

In this study, the viscosity of MgO-Water nanofluid in a different volume fraction of nanoparticles, temperatures, and shear rates has been predicted by Artificial Neural Networks (ANNs) and surface methods. In the ANN method, an algorithm is proposed to select the best neuron number for the hidden layer. In the fitting method, a surface is proposed for each volume fraction of nanoparticles, and finally, the results of ANN and surface fitting method have been compared. It can be observed that, increasing the volume fraction from 0.07% to 1.25% at temperatures of 25, 30, 40, 50, and 60 °C resulted in about two-fold increase in viscosity. Also, the best network has 24 neurons in the hidden layer. It can be seen that for a network with 24 neurons in the hidden layer has the best overall correlation, and this coefficient is 0.999035. The mean absolute value of errors in ANN and fitting method are 0.0118 and 0.0206, respectively.

Peer review status:ACCEPTED

27 Feb 2020Submitted to Mathematical Methods in the Applied Sciences
28 Feb 2020Submission Checks Completed
28 Feb 2020Assigned to Editor
28 Feb 2020Reviewer(s) Assigned
09 Mar 2020Review(s) Completed, Editorial Evaluation Pending
09 Mar 2020Editorial Decision: Revise Major
27 Mar 20201st Revision Received
27 Mar 2020Submission Checks Completed
27 Mar 2020Assigned to Editor
27 Mar 2020Reviewer(s) Assigned
27 Mar 2020Review(s) Completed, Editorial Evaluation Pending
28 Mar 2020Editorial Decision: Accept