Multi-objective Optimization to Increase Nusselt Number and Reduce Friction Coefficient of Water/Carbon Nanotubes via NSGA II using Response Surface Methodology
DOI:
https://doi.org/10.21467/jmsm.3.1.1-14Abstract
Heat transfer science is one of the most important and most applied engineering sciences, with the importance of energy management and energy conservation being doubled. Because of their properties, nanofluids have been widely used in various industries, making them particularly important to study. In this paper, the Nusselt number and coefficient of friction with volume fraction ranging from 0 to 0.1 at approximately Reynolds numbers of 200 to 5000 are studied experimentally. Higher thermal conductivity, better stability, lower pressure drop was observed using nanoparticles of solid particles. NSGA II algorithm was used to maximize Nusselt number and minimum friction coefficient by changing temperature and volume fraction of nanoparticles. To obtain Nusselt number and friction coefficient based on the temperature and volume fraction of the nanoparticles, the experimental data response surface methodology was used and with increasing Reynolds number, the Nusselt number increased and the friction coefficient decreased. In order to evaluate the objective functions in the optimization, the response surface methodology is attached to the optimization algorithm. At the end, the Pareto Front and its corresponding optimal points are presented.
Keywords:
Nusselt number; Multi-objective optimization; Nanofluids; Friction coefficient; Pareto frontDownloads
References
P. Mohammad Zadeh, T. Sokhansefat, A. B. Kasaeian, F. Kowsary, and A. Akbarzadeh, “Hybrid optimization algorithm for thermal analysis in a solar parabolic trough collector based on nanofluid,” Energy, vol. 82, pp. 857–864, 2015.
X. Wang, X. Xu, and S. U. S. Choi, “Thermal Conductivity of Nanoparticle - Fluid Mixture,” J. Thermophys. Heat Transf., vol. 13, no. 4, pp. 474–480, Oct. 1999.
A. Esmaeeli, M. Pouladian, A. Monfared, S. R. Mahdavi, and D. Moslemi, “A Genetic Algorithm and Neural Network Hybrid Model to Predict Lung Radiation-Induced Pneumonitis in Breast Radiotherapy (A simulation Study),” Babol-Jbums, vol. 16, no. 1, pp. 77–84, Jan. 2014.
M. Mohammadi, M. Dadvar, and B. Dabir, “TiO2/SiO2 nanofluids as novel inhibitors for the stability of asphaltene particles in crude oil: Mechanistic understanding, screening, modeling, and optimization,” J. Mol. Liq., vol. 238, pp. 326–340, 2017.
H. Xie, J. Wang, T. Xi, and Y. Liu, “Thermal Conductivity of Suspensions Containing Nanosized SiC Particles,” Int. J. Thermophys., vol. 23, no. 2, pp. 571–580, 2002.
J. Zhou, M. Hatami, D. Song, and D. Jing, “Design of microchannel heat sink with wavy channel and its time-efficient optimization with combined RSM and FVM methods,” Int. J. Heat Mass Transf., vol. 103, pp. 715–724, 2016.
C. Zhang et al., “Numerical and experimental studies on laminar hydrodynamic and thermal characteristics in fractal-like microchannel networks. Part A: Comparisons of two numerical analysis methods on friction factor and Nusselt number,” Int. J. Heat Mass Transf., vol. 66, pp. 930–938, 2013.
A. Akbarinia, “Impacts of nanofluid flow on skin friction factor and Nusselt number in curved tubes with constant mass flow,” Int. J. Heat Fluid Flow, vol. 29, no. 1, pp. 229–241, 2008.
N. Zhao and Z. Li, “Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids,” Materials, vol. 10, no. 5. 2017.
K. Milani Shirvan, M. Mamourian, S. Mirzakhanlari, H. F. Öztop, and N. Abu-Hamdeh, “Numerical simulation and sensitivity analysis of effective parameters on heat transfer and homogeneity of Al2O3 nanofluid in a channel using DPM and RSM,” Adv. Powder Technol., vol. 27, no. 5, pp. 1980–1991, 2016.
S. Iranmanesh, M. Mehrali, E. Sadeghinezhad, B. C. Ang, H. C. Ong, and A. Esmaeilzadeh, “Evaluation of viscosity and thermal conductivity of graphene nanoplatelets nanofluids through a combined experimental–statistical approach using respond surface methodology method,” Int. Commun. Heat Mass Transf., vol. 79, pp. 74–80, 2016.
M. Hemmat Esfe, M. H. Hajmohammad, P. Razi, M. R. H. Ahangar, and A. A. A. Arani, “The optimization of viscosity and thermal conductivity in hybrid nanofluids prepared with magnetic nanocomposite of nanodiamond cobalt-oxide (ND-Co3O4) using NSGA-II and RSM,” Int. Commun. Heat Mass Transf., vol. 79, pp. 128–134, 2016.
S.-M. Huang, C.-H. Kuo, C.-A. Chen, Y.-C. Liu, and C.-J. Shieh, “RSM and ANN modeling-based optimization approach for the development of ultrasound-assisted liposome encapsulation of piceid,” Ultrason. Sonochem., vol. 36, pp. 112–122, 2017.
P. E. Ohale, C. F. Uzoh, and O. D. Onukwuli, “Optimal factor evaluation for the dissolution of alumina from Azaraegbelu clay in acid solution using RSM and ANN comparative analysis,” South African J. Chem. Eng., vol. 24, pp. 43–54, 2017.
M. R. Sabour and A. Amiri, “Comparative study of ANN and RSM for simultaneous optimization of multiple targets in Fenton treatment of landfill leachate,” Waste Manag., vol. 65, pp. 54–62, 2017.
H. E. Patel, S. K. Das, T. Sundararajan, A. Sreekumaran Nair, B. George, and T. Pradeep, “Thermal conductivities of naked and monolayer protected metal nanoparticle based nanofluids: Manifestation of anomalous enhancement and chemical effects,” Appl. Phys. Lett., vol. 83, no. 14, pp. 2931–2933, Sep. 2003.
K. Milani Shirvan, M. Mamourian, S. Mirzakhanlari, and R. Ellahi, “Numerical investigation of heat exchanger effectiveness in a double pipe heat exchanger filled with nanofluid: A sensitivity analysis by response surface methodology,” Powder Technol., vol. 313, pp. 99–111, 2017.
A. Kumar et al., “Correlation development for Nusselt number and friction factor of a multiple type V-pattern dimpled obstacles solar air passage,” Renew. Energy, vol. 109, pp. 461–479, 2017.
T. Y. Lin and C. H. Tseng, “Optimum design for artificial neural networks: an example in a bicycle derailleur system,” Eng. Appl. Artif. Intell., vol. 13, no. 1, pp. 3–14, 2000.
M. Hatami, “Nanoparticles migration around the heated cylinder during the RSM optimization of a wavy-wall enclosure,” Adv. Powder Technol., vol. 28, no. 3, pp. 890–899, 2017.
M. Hatami, M. J. Z. Ganji, I. Sohrabiasl, and D. Jing, “Optimization of the fuel rod’s arrangement cooled by turbulent nanofluids flow in pressurized water reactor (PWR),” Chinese J. Chem. Eng., vol. 25, no. 6, pp. 722–731, 2017.
T. B. Gorji and A. A. Ranjbar, “Thermal and exergy optimization of a nanofluid-based direct absorption solar collector,” Renew. Energy, vol. 106, pp. 274–287, 2017.
Downloads
Published
Issue
Section
How to Cite
License
Copyright (c) 2020 Amin Moslemi Petrudi; Pourya Fathi, Masoud Rahmani
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Author(s) retains full copyright of their article and grants non-exclusive publishing right to this journal and its publisher "AIJR (India)". Author(s) can archive pre-print, post-print, and published version/PDF to any open access, institutional repository, social media, or personal website provided that Published source must be acknowledged with citation and link to publisher version.
Click here for more information on Copyright policy
Click here for more information on Licensing policy