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.
 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Technium: Romanian Journal of Applied Sciences and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
