Investigating and Modeling the Factors Affecting Thermal Optimization and Dynamic Viscosity of Water Hybrid Nanofluid/Carbon Nanotubes via MOPSO using ANN

Abstract

Optimization is to find the best answer among existing situations. Optimization is used in the design and maintenance of many engineering systems to minimize costs or maximize profits. Due to the widespread use of optimization in engineering, this topic has grown a lot. In this paper, the optimization of multi-objective of Water Hybrid Nanofluid/Carbon Nanotubes is investigated. Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been used in order to maximize thermal conductivity and minimum viscosity by changing the temperature (300 to 340 ºk) and the volume fraction (0.01 to 0.4%) of nanofluid. Artificial Neural Network (ANN) modeling of experimental data has been used to obtain the values. Parto fronts, the optimal points and different values are 20 members and 15 iterations, and in order to compare the results optimization process on the first, fifth, tenth fronts, a relation has been proposed to predict the viscosity and Parto fronts in the optimization process. The aim of the study was to optimize nanofluid to reduce viscosity and increase thermal conductivity.
 

Statistical modeling and investigation of thermal characteristics of a new nanofluid containing cerium oxide powder

“…They showed that the was the most influential factor among all model input parameters. Petrudi and Scurtu [ 19 ] modeled the k nf and viscosity by changing the temperature and the using ANNs. Their results show that the highest k nf and the lowest viscosity occur when a maximum temperature and are used.…”