Rigorous smart model for predicting dynamic viscosity of Al₂O₃/water nanofluid

Due to the enhanced thermophysical specifications of nanofluids, such as thermal conductivity, these types of fluids are appropriate candidates for heat transfer fluids. Nanostructure dispersion in the base fluid increases the dynamic viscosity which affects fluid flow in thermal devices. In order to facilitate design of thermal devices, it is crucial to have accurate predictive models for thermophysical properties of nanofluids. Dimensions of nanoparticles, working temperature and the concentration of nano-sized particles in the fluid are among the most influential factors in predicting dynamic viscosity of nanofluids. In the present research, four LSSVM-based algorithms including GA-LSSVM, PSO-LSSVM, HGAPSO-LSSVM and ICA-LSSVM are employed to model the dynamic viscosity of Al₂O₃/water. Results revealed that the generated models are accurate tools to calculate the dynamic viscosity of the nanofluid on the basis of the mentioned variables. The highest obtained coefficient of correlation belongs to GA-LSSVM which is equal to 0.9871, while this value for PSO-LSSVM, HGAPSO-LSSVM, and ICA-LSSVM algorithms are 0.9855, 0.9855, and 0.9846, respectively. Another utilized criterion for evaluating model accuracy is MSE value. Results revealed that the MSE values for HGAPSO-LSSVM, GA-LSSVM, PSO-LSSVM, and ICA-LSSVM are 0.00854, 0.00855, 0.00896 and 0.00979, respectively.