Heat transfer improvement has gained significant importance in the recent decades. In this regard, it is preferred to enhance the thermophysical properties of the fluids that affecting the heat transfer characteristics. To reach this goal, nanofluids have been introduced to be applied in thermal devices due to their relatively higher thermal conductivity that can cause remarkable augmentation in convective heat transfer. Thermal conductivity of these types of fluids is influenced by some elements including the temperature and volume fraction. Considering this fact, these factors must be considered for modeling this property of nanofluids. In the present article, thermal conductivity of the nanofluids with SiC particles is modeled by using artificial neural network as an intelligent method. It is observed that thermal conductivity of the nanofluids is forecasted with high precision. Mean Squared Error (MSE) of the model in optimal architecture was around 2.65× 10−5, for this network the R2 is 0.9986 revealing significant closeness of the forecasted data and corresponding experimental values.
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