Multi-objective optimization of cost and thermal performance of double walled carbon nanotubes/water nanofluids by NSGA-II using response surface method. (5th February 2017)

• Record Type: Journal Article
• Authors: Hemmat Esfe, Mohammad; Hajmohammad, Hadi; Moradi, Reza; Abbasian Arani, Ali Akbar
• Is Part Of: Applied thermal engineering. Volume 112(2017:Feb.)
• Page Start: 1648
• Page End: 1657
• Publication Date: 2017-02-05
• Subjects: Multi-objective optimization -- Response surface method -- Nanofluid -- Thermal performance factor -- Cost -- Heat transfer coefficient; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2016.10.129
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: Thermal performance of DWCNT/Water nanofluid was enhanced considering lowest cost. The multi-objective optimization method (NSGA-II) has been utilized. The optimized value of volume fraction and Reynolds number were presented. The NSGA-II was coupled with RSM to conduct the optimization. The functions of the thermal performance factors and costs were calculated by RSM. Abstract: This study was conducted with the aim of optimizing Double walls carbon nanotubes (DWCNTs)/Water nanofluid from the aspect of cost reduction and improving its thermal performance. Thermal performance factor was obtained for different volume fractions of nanoparticles and Reynolds number based on experimental data. Cost of each sample is also determined based on volume fraction of nanoparticles. Then functions of the thermal performance factors and costs were calculated by response surface method using regression higher than 0.9. Multi-objective optimization method which is effectively capable of achieving optimum answers was utilized. At the end Pareto front and optimized thermal performance factors and the corresponding lowest cost were attained. Furthermore, in order to reach the optimized pattern of lowest cost according to the most desirable thermal performance, the appropriate equation is presented. The results of optimization indicate that the cost was reduced by 38% regarding the earliest iteration.