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Journal of Enhanced Heat Transfer
Импакт фактор: 0.562 5-летний Импакт фактор: 0.605 SJR: 0.175 SNIP: 0.361 CiteScore™: 0.33

ISSN Печать: 1065-5131
ISSN Онлайн: 1026-5511

Выпуски:
Том 27, 2020 Том 26, 2019 Том 25, 2018 Том 24, 2017 Том 23, 2016 Том 22, 2015 Том 21, 2014 Том 20, 2013 Том 19, 2012 Том 18, 2011 Том 17, 2010 Том 16, 2009 Том 15, 2008 Том 14, 2007 Том 13, 2006 Том 12, 2005 Том 11, 2004 Том 10, 2003 Том 9, 2002 Том 8, 2001 Том 7, 2000 Том 6, 1999 Том 5, 1998 Том 4, 1997 Том 3, 1996 Том 2, 1995 Том 1, 1994

Journal of Enhanced Heat Transfer

DOI: 10.1615/JEnhHeatTransf.2020032595
pages 123-141

GENETIC ALGORITHM MULTIOBJECTIVE OPTIMIZATION OF A THERMAL SYSTEM WITH THREE HEAT TRANSFER ENHANCEMENT CHARACTERISTICS

Reza Beigzadeh
Department of Chemical Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
Smith Eiamsa-ard
Department of Mechanical Engineering, Faculty of Engineering, Mahanakorn University of Technology, Bangkok 10530, Thailand

Краткое описание

A heat transfer enhancement system including CuO/water nanofluid in a corrugated tube equipped with twisted tape was modeled by two well-known artificial neural network techniques. The multilayer perceptron and group method of data handling neural networks were employed to predict thermal-hydraulic characteristics as functions of main operating conditions. In addition, the genetic algorithm (GA) approach was used to develop applied empirical correlations. The purpose of the models is to estimate Nusselt number (Nu) and friction factor (f) in the investigated heat exchanger. The main effective parameters investigated in this study are volume fraction of nanoparticle, twist ratios of twisted tape, and Reynolds number. According to the conflicting relationship between heat transfer and pressure drop, the more accurate model was selected as the objective functions for multi-objective optimization by GA. The optimum operating conditions of the investigated heat exchangers that lead to a trade-off between Nu and f were proposed.

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