Prediction of Thermal Conductivity and Viscosity of Ionic Liquid-Based Nanofluids Using Adaptive Neuro Fuzzy Inference System

Nowadays, ionic liquid-based nanofluids are introduced as a new class of heat transfer fluids, which exhibit superior thermal properties compared to their base ionic liquids. Potential applications of these nanofluids make it necessary to know their thermophysical properties such as thermal conductivity and viscosity. Therefore, adaptive neuro fuzzy inference system (ANFIS) has been successfully developed to predict thermal conductivity and viscosity of ionic liquid-based nanofluids. The developed models have investigated the influence of temperature, nanoparticle concentration, and ionic liquid molecular weight on the thermophysical properties of nanofluids. After developing ANFIS structure, the capability and accuracy of the developed neuro fuzzy models have been evaluated by comparison of model predictions with experimental data extracted from the literature and calculation of statistical parameters such as coefficient of determination (R2) and average relative deviation (ARD). The ARD of ANFIS model in prediction of thermal conductivity of nanofluids is 0.72%, with a high R2 of 0.9959. The values of ARD and R2 for estimation of nanofluids viscosity are 5.1% and 0.9934, respectively, which indicates a satisfactory degree of accuracy for the proposed models.