Journal
APPLIED SOFT COMPUTING
Volume 130, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2022.109644
Keywords
Hybrid nanofluid; Thermo -physical properties; Support vector regression; Genetic optimization
Ask authors/readers for more resources
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.
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
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available