Evolutionary optimization of thermo-physical properties of MWCNT-Fe3O4/water hybrid nanofluid using least-squares support vector regression-based models

Decisions on optimizing design and operating parameters are challenging when using hybrid nanofluids (HNFs). A procedure is proposed and implemented for predicting and optimizing the thermal conductivity and dynamic viscosity of MWCNT-Fe3O4/water HNF. The procedure involves using precise least-squares support vector regression (LSSVR) models, multi-objective genetic optimization of thermal properties, and automated selection of optimal design conditions. Tuned parameters are the volume fractions of nanoparticles and the operating temperature. The cross-validated and carefully optimized LSSVR models for thermal conductivity and dynamic viscosity showed excellent performances, with testing mean percentage errors of -0.246 and -0.103%, and relative root mean square errors of 1.325 and 2.165%, respectively. By assigning equal importance to the two response

Automated summary

This study proposes and implements a method for predicting and optimizing the thermal conductivity and dynamic viscosity of hybrid nanofluids (HNFs). By using LSSVR models and multi-objective genetic optimization of thermal properties, excellent predictive results are achieved.