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Prediction of nanofluid viscosity using multilayer perceptron and Gaussian process regression

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Abstract

More than a decade, a numerous experimental and theoretical studies of thermophysical properties of nanofluids are conducted to reveal its heat transfer characteristics. Due to nanofluid unique thermal properties, it is broadly used in various applications from automobile applications to biomedical applications. Despite that various experimental and theoretical studies of nanofluids are developed, the accordance between them is very little and also it is tiresome and expensive. To predict the thermal properties in an easy way, soft computing tools are utilized. In this research work, dynamic viscosity ratio of Al2O3/H2O is predicted using machine learning techniques like multilayer perceptron and Gaussian process regression. In the proposed multilayer perceptron—artificial neural network model, varying a range of neurons in the hidden layer and using Levenberg–Marquardt as training function, it is found that 6 neurons in the hidden layer give less root mean square error value of 0.01118. Different kernel functions are opted to train the proposed Gaussian process regression model, and it is found that Matern kernel function shows the best performance with less root mean square error value of 0.018, and regression coefficient value of both the models is 0.99. This research work will reduce the experimental test run cost, and the models are accurate in prediction.

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Abbreviations

GPR:

Gaussian process regression

DVR:

Dynamic viscosity ratio

MLP:

Multilayer perceptron

ANN:

Artificial neural network

H2O:

Water

Al2O3 :

Alumina oxide

RMSE:

Root mean square error

NMSE:

Normalized mean square error

MAPE:

Mean absolute percentage error

R 2 :

Regression coefficient value

MSE:

Mean squared error

MAE:

Mean absolute error

µ p :

Dynamic viscosity ratio of predicted data

µ a :

Dynamic viscosity ratio of experimental data

\(\bar{\mu }_{\text{a}}\) :

Mean value of dynamic viscosity ratio of experimental data

n :

Total number of data samples

T :

Temperature (K)

ɸ :

Volume fraction

D :

Size of nanoparticle (nm)

σ :

Standard deviation

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Authors and Affiliations

  1. University College of Engineering, Dindigul, Tamilnadu, 624 622, India

    P. C. Mukesh Kumar

  2. Parisutham Institute of Technology and Science, Thanjavur, Tamilnadu, 613 005, India

    R. Kavitha

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  1. P. C. Mukesh Kumar
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  2. R. Kavitha
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Correspondence to R. Kavitha.

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Mukesh Kumar, P.C., Kavitha, R. Prediction of nanofluid viscosity using multilayer perceptron and Gaussian process regression. J Therm Anal Calorim 144, 1151–1160 (2021). https://doi.org/10.1007/s10973-020-09990-4

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  • DOI: https://doi.org/10.1007/s10973-020-09990-4

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