Stability and Thermal Property Optimization of Propylene Glycol-Based MWCNT Nanofluids

Propylene glycol-based MWCNT(multi-walled carbon nanotubes)nanofluids were prepared in the framework of a two-step method and by using a suitable PVP(polyvinyl pyrrolidone)dispersant.The BBD(Box-Behnken design)model was exploited to analyze 17 sets of experiments and examine the sensitivity of the absorbance to three parameters,namely the concentration of MWCNT,the SN ratio(mass ratio of carbon nanotubes to sur-factants)and the sonication time.The results have revealed that,while the SN ratio and concentration of MWCNT have a strong effect on the absorbance,the influence of the sonication time is less important.The sta-tistical method of analysis of variance(ANOVA)was further used to determine the F-and p-values of the model.Five experiments were run to validate this approach.Since sample 2 was found to display the greatest absorbance,it was selected for stability monitoring as well as thermal conductivity and viscosity measurements.This sample has been found to be stable;the viscosity decreased with increasing temperature;the addition of MWCNT nano-particles was more effective in improving the thermal conductivity of propylene glycol than other methods in the literature.Moreover,the MWCNT nanofluid based on propylene glycol exhibited higher thermal conductivity at low temperatures.

Prediction and Optimization of the Thermal Properties of TiO_(2)/Water Nanofluids in the Framework of a Machine Learning Approach

In this study,comparing multiple models of machine learning,a multiple linear regression(MLP),multilayer feed-forward artificial neural network(BP)model,and a radial-basis feed-forward artificial neural network(RBF-BP)model are selected for the optimization of the thermal properties of TiO_(2)/water nanofluids.In particular,the least squares support vector machine(LS-SVM)method and radial basis support vector machine(RB-SVM)method are implemented.First,curve fitting is performed by means of multiple linear regression in order to obtain bivariate correlation functions for thermal conductivity and viscosity of the nanofluid.Then the aforementioned models are used for a predictive analysis of the dependence of its thermal conductivity and viscosity on temperature and volume fraction.The results show that the least squares support vector machine(LS-SVM)has a prediction accuracy higher than the other models.The model predicts the thermal conductivity of TiO_(2)/water MSE=1.0853×10^(-6),R2=0.99864,MAE=0.00092,RMSE=0.00104,and the viscosity of TiO_(2)/water MSE=8.1397×10^(-6),R2=0.99995,MAE=0.00074,RMSE=0.0009.