Investigation on thermal conductivity and viscosity of nanofluids using analytical and machine learning models

TL;DR

This study compares analytical and machine learning models for predicting the thermal conductivity and viscosity of nanofluids, finding that ANN models outperform traditional analytical approaches in accuracy.

Contribution

It introduces machine learning models, especially ANN, for predicting nanofluid properties, enhancing accuracy over existing analytical models.

Findings

ANN models predict viscosity more accurately than analytical models.

Linear regression and ANN models predict thermal conductivity closely.

Machine learning models improve prediction accuracy of nanofluid properties.

Abstract

Knowledge of thermal properties is essential to design and evaluate thermal systems and processes using nanofluids. This paper presents different analytical models to predict thermal conductivity and viscosity. The efforts have been made to develop machine learning models to predict these properties. An extensive literature survey was carried out to collect thermal properties data of different nanofluids to train and test these machine learning models. The most influential properties like thermal conductivity, diameter, volume concentration of nanoparticles, base fluid thermal conductivity and nanofluid temperature are used as input variables to the thermal conductivity models and molecular weight, diameter and volume fraction nanoparticles, base fluid viscosity and nanofluid temperature are taken as an input variable to the viscosity model. Data is divided into two-part, one part is used to train the models and remaining part is used to test it. Result shows linear regression and ANN model do predict thermal conductivity more closely and ANN predict viscosity more accurately compared to analytical models.