In order to enhance the thermal properties of turbine oil (TO), three different nanoparticles (CuO, Al2O3, and TiO2) are loaded into the TO. To measure the thermal performance of nanoparticles-based TO nanofluids at laminar flow and constant heat flux boundary condition, an experimental setup was applied. The obtained data clearly demonstrate the positive effect of all nanoparticles on the heat transfer rate of TO. As the most important factor, heat transfer coefficient of above-mentioned two phase systems is increased with increasing both volume concentration and flow rate. Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied for modelling the effect of critical parameters on the heat transfer coefficient of nanoparticles-TO based nanofluid numerically. The results compared with experimental ones for training and test data. Results suggest that the developed model is enough valid and promising for predicting the extant of heat transfer coefficient. R2 and MSE values for all data were 0.990208751 and 108.1150734, respectively. Based on the results, it is obvious that our proposed modelling by ANFIS is efficient and valid, which can be expanded for more general states.

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