Toward a modeling study of thermal conductivity of nanofluids using LSSVM strategy

In the present study, a comprehensive model based on least square support vector machine algorithm (LSSVM) was developed to estimate thermal conductivity of nanofluids. The model assessed the thermal conductivity of 29 different nanofluids. The representative nanofluids were composed of nine base fluids, including water, ethylene glycol, transformer oil, engine oil, R113, DI Water, monoethylene glycol, paraffin, and oil. Al2O3, TiO2, CuO, ZnO, Al, and Cu nanoparticles were employed in the corresponding nanofluids. A collection of 1109 experimental samples from reliable sources was used. In addition, the present model can estimate the thermal conductivity of nanofluids as a function of temperature, diameter, nanoparticle volume fraction as well as the thermal conductivity of the nanoparticles and the base fluid. The proposed LSSVM structure was optimized by particle swarm optimization technique where the outcomes proved great accuracy of the model for estimating the thermal conductivity of nanofluids. Moreover, statistical observations showed superior predictive ability of LSSVM model than other previous available correlations. Namely, the average relative deviation percent of 2.46 and 3.10%, and R-squared values of 0.9954 and 0.9914 were resulted for training and testing stages of LSSVM model, respectively.