R. Kavitha - Parisutham Institute of Technology and Science, Thanjavur-۶۱۳ ۰۰۵, Tamil Nadu, India
P. C. Mukesh Kumar - University College of Engineering Dindigul-۶۲۴ ۶۲۲, Tamil Nadu, India

Desirable thermal properties of nanofluid is the vital reason for using nanofluid. There is an exemplary development in various applications using nanofluid. Mathematical and experimental models were developed to predict the thermal properties of nanofluids, the models are tiresome and expensive and have discrepancies between them. Soft computing tools are most useful in prediction, classification and clustering the data with good accuracy and with less expensive. In this paper, comparative analysis of Multi Layer Perceptron (MLP) model and Support Vector Regression (SVR) model were done by using various evaluation criterions. The two models developed to predict the thermal conductivity ratio of CNT/H۲O and the results were compared. The present modeling has been carried out using MATLAB ۲۰۱۷ b. In both the models, the experimental values and predicted values possess good accordance. Regression coefficient value (R۲) for overall data is ۰.۹۹ and ۰.۹۸ for MLP and SVR models respectively. The Root Mean Square Error (RMSE) value is less in MLP model when compared with SVR model, RMSE values are ۰.۰۱۵۷۸ and ۰.۰۱۸۱۲ respectively. The prediction is best in MLP model but with limited experimental data set, it fails to address over fitting problem, whereas SVR model is ideal with limited data set, it overcomes over fitting problem and possess better generalization than MLP model.

Nano fluids, Thermal conductivity, Artificial neural network, Multilayer Perceptron, Support Vector Regression