Contraction of radiator length in heavy vehicles using cerium oxide nanofluid by enhancing heat transfer performance

Senthil, Ramalingam; Dhairiyasamy, Ratchagaraja; Rajendran, Silambarasan; Radhakrishnan, Manikandan

doi:10.2298/tsci16s4037r

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…The database includes experimental data-sets collected from various works of literature. In this study, 523 and 302 experimental data samples (Abdollahi et al, 2018;Agarwal et al, 2017;Azmi et al, 2012;Barbés et al, 2013;Buonomo et al, 2015;Colla et al, 2015;Ferrouillat et al, 2013;Ghodsinezhad et al, 2016;Ho et al, 2010;Kim et al, 2007;Kumar et al, 2016;Madhesh et al, 2016;McCants et al, 2009;Pak and Cho, 2007;Pantzali et al, 2009;Pastoriza-Gallego et al, 2009;Patil et al, 2018;Paul et al, 2013;Ramalingam et al, 2016;Saxena et al, 2016;Suresh et al, 2016;Tso and Chao, 2015;Zhu et al, 2011) were taken for the model development of viscosity and thermal conductivity of nanofluids, respectively, which have been extracted from the database. Data includes various nanofluids namely Copper Oxide/water, Aluminium oxide/water, Titanium dioxide/water, Zinc oxide/water, Silver/water, Silicon dioxide/water, and Cerium oxide/water.…”

Section: Data-setmentioning

confidence: 99%

Modelling of Viscosity and Thermal Conductivity of Water-Based Nanofluids using Machine-Learning Techniques

Ganga,

Uddin,

Asthana

et al. 2023

Int. j. math. eng. manag. sci.

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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.

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Section: Data-setmentioning

confidence: 99%

Modelling of Viscosity and Thermal Conductivity of Water-Based Nanofluids using Machine-Learning Techniques

Ganga,

Uddin,

Asthana

et al. 2023

Int. j. math. eng. manag. sci.

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Comparative analysis of machine learning techniques for estimating dynamic viscosity in various nanofluids for improving the efficiency of thermal and radiative systems

Snoussi,

Fakhfakh,

Khedher

et al. 2025

Journal of Radiation Research and Applied Sciences

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Contraction of radiator length in heavy vehicles using cerium oxide nanofluid by enhancing heat transfer performance

Abstract: De part ment of Me chan i cal En gi neer ing, Uni ver sity Col lege of En gi neer ing Villupuram, Villupuram, Tamil Nadu, In dia Orig i nal sci en tific pa per

Cited by 2 publications

References 17 publications

Modelling of Viscosity and Thermal Conductivity of Water-Based Nanofluids using Machine-Learning Techniques

Modelling of Viscosity and Thermal Conductivity of Water-Based Nanofluids using Machine-Learning Techniques

Comparative analysis of machine learning techniques for estimating dynamic viscosity in various nanofluids for improving the efficiency of thermal and radiative systems

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