M. Sahooli - Nano Chemical Eng. Dep., Faculty of Advanced Technologies, Shiraz University, Shiraz, Iran.
S. Sabbaghi - Nano Chemical Eng. Dep., Faculty of Advanced Technologies, Shiraz University, Shiraz, Iran.
R. Maleki - Nano Chemical Eng. Dep., Faculty of Advanced Technologies, Shiraz University, Shiraz, Iran.
M. M. Nematollahi - School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.

Statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learning models were proposed to represent the thermal conductivity as a function based on the temperature, nanoparticles volume fraction and the thermal conductivity of the nanoparticles. The results of models were in appropriate agreement with the experimental data. This work represents ۸ machine learning models for the predicting the thermal conductivity of water-based nanofluids. The models have been trained and tested on two separate sets of data. Three metrics have been employed to evaluate the performance of the models. The best method for each system is selected using results.

Nanofluids, Modeling, Machine Learning, Thermal conductivity, prediction