A correlation to predict the thermal conductivity of MXene‐silicone oil based nano‐fluids and data driven modeling using artificial neural networks

In this article, MXene nano-material is used with silicone oil to improve the thermal conductivity of nano-fluids and it further increases the heat transfer. MXene nano-material-silicone oil-based nano-fluids have the capacity to be employed as heat transfer fluids in various thermal applications since it has excellent thermal properties, higher heat resistance and silicone oil have the ability to operate upto a temperature of 400°C. This paper investigates the thermal conductivity values for different concentrations and temperature of MXene nano-fluids. The thermal conductivity of the prepared MXene nano-fluids is strongly dependent on temperature and concentration of MXene nano-particles. The values of thermal conductivity are predicted by developing multilayer perceptron (MLP) Artificial Neural Network (ANN) model. The temperature and nano-particles concentration are taken as inputs and thermal conductivity is taken as output to the model. Mean square error (MSE) and correlation coefficient (R) are used to assess the efficacy of the developed model using trial and error method. The values of MSE and correlation coefficient for the optimum model are found to be 2.6 × 10−07 and 0.99687, respectively. A correlation for thermal conductivity as a function of temperature and concentration is also propounded using the experimental results. The developed optimum ANN model show satisfactory performance, and the predicted thermal conductivity values matches well with experimental thermal conductivity values.