Smart tracking of the influence of alumina nanoparticles on the thermal coefficient of nanosuspensions: application of LS-SVM methodology

The thermal conductivity of working fluids is among the most important thermophysical property in all heat transfer equipment. Accurate estimation of the nano-fluids thermal conductivity is a prerequisite for designing and optimizing the associated heat-based equipment. Therefore, the present study simulates the thermal conduction coefficients of water–alumina nano-suspensions using the least-squares support vector machines (LS-SVM). The best structure of this paradigm is determined using a combination of trial-and-error and statistical analyses. After that, it is validated by both available empirical correlations and intelligent models available in the open literature. Our LS-SVM paradigm predicted 282 experimental data samples available in fifteen references with the absolute average relative deviation (AARD) of 1.24%, mean squared errors (MSE) of 0.0007, root mean squared errors (RMSE) of 0.026, and regression coefficient (R2) of 0.9586. The leverage technique justifies that minor parts of experimental data are outliers (~ 6.03%) and have an insignificant negative effect on the derived LS-SVM generalization. The designed simulator shows that temperature and alumina concentration positively affect the nano-fluids thermal conductivity, and alumina size reduces the thermal behavior of water–alumina nano-suspensions.