Modelling of Viscosity and Thermal Conductivity of Water-Based Nanofluids using Machine-Learning Techniques
-
Published:2023-10-01
Issue:5
Volume:8
Page:817-840
-
ISSN:2455-7749
-
Container-title:International Journal of Mathematical, Engineering and Management Sciences
-
language:en
-
Short-container-title:Int. j. math. eng. manag. sci.
Author:
Ganga Sai1, Uddin Ziya1, Asthana Rishi1, Hassan Hamdy2, Bhardwaj Arpit1
Affiliation:
1. School of Engineering and Technology, BML Munjal University, Gurugram, 122413, Haryana, India. 2. Energy Resources Engineering Department, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt.
Abstract
In this study, a variety of machine-learning algorithms are used to predict the viscosity and thermal conductivity of several water-based nanofluids. Machine learning algorithms, namely decision tree, random forest, extra tree, KNN, and polynomial regression, have been used, and their performances have been compared. The input parameters for the prediction of the thermal conductivity of nanofluids include temperature, concentration, and the thermal conductivity of nanoparticles. A three-input and a two-input model were utilized in modelling the viscosity of nanofluid. Both models considered temperature and concentration as input parameters, and additionally, the type of nanoparticle was considered for the three-input model. The order of importance of the most influential parameters in predicting both viscosity and thermal conductivity was studied. A wider range of input parameters have been considered in an open-access database. With the existing experimental data, all of the developed machine learning models exhibit reasonable agreement. Extra trees were found to provide the best results for estimating thermal conductivity, with a value of 0.9403. In predicting viscosity using a three-input model, extra trees were found to provide the best result with a value of 0.9771, and decision trees were found to provide the best results for estimating the viscosity using a two-input model with a value of 0.9678. In order to study heat transport phenomena through mathematical modelling, it is important to have an explicit mathematical expression. Therefore, the formulation of mathematical expressions for predicting viscosity and thermal conductivity has been carried out. Additionally, a comparison with the Xue and Maxwell thermal conductivity models is made to validate the results of this study, and the results are observed to be reliable.
Publisher
Ram Arti Publishers
Subject
General Engineering,General Business, Management and Accounting,General Mathematics,General Computer Science
Reference60 articles.
1. Abdollahi, A., Karimi Darvanjooghi, M.H., Karimipour, A., & Safaei, M.R. (2018). Experimental study to obtain the viscosity of CuO-loaded nanofluid: effects of nanoparticles’ mass fraction, temperature and basefluid’s types to develop a correlation. Meccanica, 53, 3739-3757. 2. Afrand, M., Toghraie, D., & Sina, N. (2016). Experimental study on thermal conductivity of water-based Fe3O4 nanofluid: Development of a new correlation and modeled by artificial neural network. International Communications in Heat and Mass Transfer, 75, 262-269. 3. Agarwal, R., Verma, K., Agrawal, N.K., & Singh, R. (2017). Sensitivity of thermal conductivity for Al2O3 nanofluids. Experimental Thermal and Fluid Science, 80, 19-26. 4. Ahmadi, M.H., Baghban, A., Ghazvini, M., Hadipoor, M., Ghasempour, R., & Nazemzadegan, M.R. (2020a). An insight into the prediction of TiO2/water nanofluid viscosity through intelligence schemes. Journal of Thermal Analysis and Calorimetry, 139, 2381-2394. 5. Ahmadi, M.H., Mohseni-Gharyehsafa, B., Farzaneh-Gord, M., Jilte, R.D., Kumar, R., & Chau, K.W. (2019). Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms. Engineering Applications of Computational Fluid Mechanics, 13(1), 220-228.
|
|