Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids

Abstract

Nanofluids have recently been considered as one of the most popular working fluid in heat transfer and fluid mechanics. Accurate estimation of thermophysical properties of nanofluids is required for the investigation of their heat transfer performance. Thermal conductivity coefficient, convective heat transfer coefficient, and viscosity are some the most important thermophysical properties that directly influence on the application of nanofluids. The aim of the present chapter is to develop and validate artificial neural networks (ANNs) to estimate these thermophysical properties with acceptable accuracy. Some simple and easy measurable parameters including type of nanoparticle and base fluid, temperature and pressure, size and concentration of nanoparticles, etc. are used as independent variables of the ANN approaches. The predictive performance of the developed ANN approaches is validated with both experimental data and available empirical correlations. Various statistical indices including mean square errors (MSE), root mean square errors (RMSE), average absolute relative deviation percent (AARD%), and regression coefficient (R2) are used for numerical evaluation of accuracy of the developed ANN models. Results confirm that the developed ANN models can be regarded as a practical tool for studying the behavior of those industrial applications, which have nanofluids as operating fluid.

Critical points and stability analysis in MHD radiative non‐Newtonian nanoliquid transport phenomena with artificial neural network prediction

“…Sabir et al 35 investigated the solution of singular three‐point second‐order boundary value problems by utilizing novel meta‐heuristic computing solver. The numerical or experimental results with ANN modeling in nanofluids has been examined by the researchers in recent articles 36–40 …”

“…The numerical or experimental results with ANN modeling in nanofluids has been examined by the researchers in recent articles. [36][37][38][39][40] In the current analysis, the subsequent dimensionless nanofluid model is constructed using Lie symmetry technique and solved computationally with shooting technique for predicting multiple solutions, followed by temporal stability analysis. The impact of Maxwell nanofluids transport phenomena induced by stretching/shrinking surface in presence of chemical reaction and heat source/sink under various administering conditions has been investigated.…”