Application of M5 tree regression, MARS, and artificial neural network methods to predict the Nusselt number and output temperature of CuO based nanofluid flows in a car radiator. (July 2020)

• Record Type: Journal Article
• Authors: Kahani, Mostafa; Ghazvini, Mahyar; Mohseni-Gharyehsafa, Behnam; Ahmadi, Mohammad Hossein; Pourfarhang, Amin; Shokrgozar, Motahareh; Zeinali Heris, Saeed
• Is Part Of: International communications in heat and mass transfer. Volume 116(2020:Aug.)
• Publication Date: 2020-07
• Subjects: CuO nanoparticles -- Artificial neural network -- Nusselt number -- Car radiator -- Multi-Variate adaptive regression splines (MARS) -- M5 tree regression; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Heat -- Transmission; Mass transfer; Periodicals; 621.4022
• DOI: 10.1016/j.icheatmasstransfer.2020.104667
• Languages: English
• ISSNs: 0735-1933

Abstract

Abstract: In the current study, CuO nanoparticles were dispersed in a mixture of Ethylene Glycol-Water (60/40 wt. %) to prepare stable nanofluid in different concentrations (0.05 − 0.8 vol. %). The samples were used as the coolant fluid in a specific car radiator to evaluate the thermal performance of nanofluid and base fluid in the system. Five different and novel Machine-learning methods were applied over experimental data to predict the Nusselt number and output temperature of the coolant in the system. These methods are M5 tree regression, Linear and Cubic Multi-Variate Adaptive Regression Splines (MARS), Radial Basis Function (RBF), and Artificial Neural Network-Levenberg Marquardt Algorithm (ANN-LMA). Although all studied methods show acceptable accuracy in predicting experimental data, the ANN-LMA method in output temperature modeling and the MARS-Linear method in Nusselt number modeling has more precision.