Meta-heuristic and RSM approach for Multi-objective Optimization of Plain Flow Refrigerant Vapor Condensation inside Tubes

Yıl 2018, Cilt: 21 Sayı: 2, 94 - 101, 30.05.2018

Ravindra Kumar Parmananad Kumar

https://doi.org/10.5541/ijot.306393

Öz

Condensers are extensively used heat exchangers in
automobiles and air conditioning systems. Optimization of heat transfer and
pressure drop inside condensers is an important area of concern for the designers.
In the present study, condensation  characteristics inside smooth
horizontal tubes is optimized using teaching-learning based optimization (TLBO)
algorithm and response surface methodology (RSM). Refrigerant mass velocity
(G), vapor quality (x) and tube internal diameter (D_i) are taken as
design parameters. Heat transfer coefficient (h) and pressure drop (ΔP) values of
refrigerants are calculated based on the mathematical models and served as
objective functions for RSM. The same mathematical models are applied to
formulate a multi-objective optimization problem with an aim to maximize heat
transfer coefficient and minimize pressure drop and is solved using TLBO. Two
different refrigerants have been considered to display the application of the
approaches. Of the two methods applied, TLBO seems to give better optimum
results.

Anahtar Kelimeler

Condensation, Heat transfer, Pressure drop, TLBO, RSM

Kaynakça

• Citation1 Md. Anowar Hossain, Yoji Onaka and Akio Miyara, Experimental study on condensation heat transfer and pressure drop in horizontal smooth tube for R-1234ze(E), R-32and R-410A, International Journal of Refrigeration, 35 (2012) 927-938. Citation 2 Feng Xing, Jinliang Xu, Jian Xie, Huan Liu, Zixuan Wang and Xiaolin Ma, Froude number dominates condensation heat transfer of R-245fa in tubes: Effect of inclination angles, International Journal of Multiphase Flow, 71 (2015) 98–115. Citation 3 M. Mohammed Shah, An Improved and Extended General Correlation for Heat Transfer During Condensation in Plain Tubes, HVAC&R RESEARCH,15(2009),No.5. Citation 4 M. K. Dobson and J.C. Chato, Condensation in Smooth Horizontal Tubes, Journal of Heat Transfer, 120/193 (1198) Citation 5 A.S. Dalkilic and S. Wongwise, Intensive literature review of condensation inside smooth and enhanced tubes, International Journal of Heat and Mass Transfer, 32(2009) 3409-3426. Citation6 A.K. Kahtua, P. Kumar, H.N. Singh and R. Kumar, Measurement of enhanced heat transfer coefficient with perforated twisted tape inserts during condensation of R-245fa, Heat Mass Transfer, 52(2016) 683–691 Citation7 Jingzhi Zhang, Wei Li and W. J. Minkowycz, Numerical simulation of condensation for R-410A at varying saturation temperatures in mini/micro tubes, Numerical Heat Transfer, PART A, 69(2016) 464–478. Citation8 Sepher Sanaye and Masoud Dehghandokht, Modeling and multi-objective optimization of parallel flow condenser using evolutionary algorithm, Applied Energy, 88(2011) 1568-1577. Citation9 V.K. Patel and R.V. Rao, Design optimization of shell and tube heat exchangers using particle swarm optimization technique, Applied Thermal Energy, 30(2010) 1417-1425. Citation10 K. Baadache and C. Bougriou, Optimization of the design of shell and double tube concentric tubes heat exchanger using the genetic algorithm”, Heat Mass Transfer, 51(2015)1371-1381. Citation11 Hassan Hajabdollahi, Pouria Ahmadi and Ibrahim Dincer, Thermo economic optimization of a shell and tube condenser using both genetic algorithm and particle swarm, International Journal of Refrigeration, 34(2011) 1066-1076. Citation12 R.V. Rao and Gajanan Waghmare, Optimization of thermal performance of a smooth flat-plate solar air heater using teaching-learning-based optimization algorithm, Cogent Engineering 2(2015) 997421. Citation13 Ravindra Kumar and P. Kumar, Optimization of Heat Transfer Coefficient during Condensation of Refrigerant inside Plain Horizontal Tube using Teaching-Learning based Optimization Algorithm, Indian Journal of Science and Technology, 9(38), October 2016 Citation14 Vivek Patel and Vimal Savsani, Optimization of a plate-fin heat exchanger design through an improved multi-objective teaching-learning based optimization (MOITLBO) algorithm, Chemical Engineering Research and Design, 92 (2014) 2371–2382. Citation15 H. Safikhani, A. Abbassi, A. Khalkhali, and M. Kalteh, Modeling and Optimization of Nanofluid Flow in Flat Tubes Using a Combination of CFD and Response Surface Methodology, Heat Transfer-Asian Research, 00 (0), 2014. Citation16 Kamel Milani Shirvan , Mojtaba Mamourian, Soroush Mirzakhanlari and R. Ellahi, Two phase simulation and sensitivity analysis of effective parameters on combined heat transfer and pressure drop in a solar heat exchanger filled with nanofluid by RSM, Journal of Molecular Liquids, 220 (2016) 888–901. Citation17 Huai-Zhi Han, Bing-Xi Li, Hao Wu and Wei Shao, Multi-objective shape optimization of double pipe heat exchanger with inner corrugated tube using RSM method, International Journal of Thermal Sciences, 90 (2015) 173-186. Citation18 Abdussamet Subasi , Bayram Sahin and Irfan Kaymaz, Multi-objective optimization of a honeycomb heat sink using Response Surface Method, International Journal of Heat and Mass Transfer, 101 (2016) 295–302. Citation19 R.V. Rao, V. Savsani and D. P. Vakharia, Teaching-learning based optimization: a novel method for constrained mechanical design optimization problem, Computer added Design, 43 (2011) 303-315. Citation20 R. V. Rao, V. Savsani, and D .P. Vakharia, Teaching learning based optimization: an optimization method for non-linear large scale problem, Information Sciences, 183 (2012)1-15. Citation21 Box, G., & Hunter, J. (1957). Multi-Factor Experimental Designs for Exploring Response Surfaces. The Annals of Mathematical Statistics, 28(1), 195-241. Retrieved from http://www.jstor.org/stable/2237033.