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Dergiler / Bitlis Eren University Journal of Science and Technology / 2017 / Cilt: 7 - Sayı: 1

Multi-objective thermal design optimization of plate frame heat exchangers through Global Best Algorithm

Oguz Emrah TURGUT
Sayfa
33–73
Yayın Tarihi
2017-06-13
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It is observed that selected design variables has a signficant effect on problem objectives.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study deals with thermal design of plate frame heat exchangers based on Global Best Algorithm.
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Temel Bulgular & Veri:Firstly, optimization performance of the proposed algorithm has been benchmarked against variety of well-known optimization algorithms by means of 16 different highly challenging optimization test functions.
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Sonuç & Katkı:It is observed that selected design variables has a signficant effect on problem objectives.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Decision making methods · Multi-objective optimization · Optimization algorithms

Abstract

This study deals with thermal design of plate frame heat exchangers based on Global Best Algorithm. By utilizing some basic perturbation schemes adopted from Differential Search and Differential Evolution, Global Best Algorithm aims to obtain optimum solution of any optimization problem with intensifying on exploitiation of the promising solutions rather than exploring of the unvisited paths of the search domain. Firstly, optimization performance of the proposed algorithm has been benchmarked against variety of well-known optimization algorithms by means of 16 different highly challenging optimization test functions. Then, the proposed method is put into practice to acquire the optimal values of the design variables those optimize the considered problem objectives including overall heat transfer coefficient, total cost and weight of the plate frame heat exchangers seperately as well as simultaneously. Considerable improvement in objective function values is observed as compared to preliminary design in single objective manner. Pareto frontier is constructed for dual and triple objective and best optimal solution among the curve is selected by means of the widely-known decision making methods of LINMAP, TOPSIS, and Shannon’s entropy theory. Optimal results obtained from each decision making theory are compared with respect to their corresponding deviation indexes and the best one is preferred. A sensitivity analysis is then performed to study the variational influences of some design parameters on the considered objective functions. It is observed that selected design variables has a signficant effect on problem objectives.

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Bu Makaleye Atıf Yap

TURGUT, O. E. (2017). Multi-objective thermal design optimization of plate frame heat exchangers through Global Best Algorithm. Bitlis Eren University Journal of Science and Technology, Cilt: 7 - Sayı: 1, 33–73. https://ajindex.com/articles/743807

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Kaynakça (15)

  1. Amini M, Bazargan M (2014). Two objective optimization in shell-and-tube heat exchangers using genetic algorithm. Appl Therm Eng 69, 278-285.
  2. Arora R, Kaushik SC, Kumar R, Arora R (2016). Soft computing based multi objective optimization of Brayton cycle power plant with isothermal heat addition using evolutionary algorithm and decision making. Appl Soft Comput 46, 267 – 283. Arora R, Kaushik SC, Arora R (2016). Thermodynamic modelling and multi-objective optimization of two stage thermoelectric generator in electrically series and parallel configuration. Appl Therm Eng 103, 1312-1323.
  3. Askarzadeh A (2014). Bird mating optimizer: An optimization algorithm inspired by bir mating strategies. Commun Nonlinear Sci 19,1213-1228.
  4. Asadi M, Song Y, Sunden G, Xie G (2014). Economic optimization design of shell and tube heat exchangers by a cuckoo search algorithm. Appl Therm Eng 73, 1032-1040.
  5. Brest J, Greiner S, Boscovic B, Mernik M, Zumer V (2006). Self adaptive control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE T Evol Comput 10, 646-657.
  6. Civicioğlu P (2013). Artifical cooperative search algorithm for numerical optimization problems. Inform Sci 229, 58-76.
  7. Civicioglu P (2013). Backtracking Search Optimization Algorithm for numerical optimization problems. Appl Math Comput 219, 8121-8144.
  8. Civicioglu P (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geo 46, 229 – 247. Edwards MF, Changal AA, Parott DL (1974). Heat transfer and pressure drop characteristics of a plate heat exchanger using Newtonian and non-Newtonan liquids. Chem Eng 286-293.
  9. Erol OK, Eksin I (2006). A new optimization method: Big Bang – Big Crunch. Adv Eng Softw. 37, 106-111.
  10. Fettaka S, Thibault J, Gupta Y (2013). Design of shell and tube heat exchangers using multi objective optimization. Int J Heat Mass Transfer. 60, 343-354.
  11. Focke WW (1986). Selecting optimum plate heat exchanger surface pattern. J Heat Transfer 108, 153-160.
  12. Galeazzo FCC, Miura RY, Gut JAW, Tadini CC (2006). Experimental and numerical heat transfer in a plate heat exchanger. Chem Eng Sci 61, 7133-7138.
  13. Gamperle R, Müller SD, Koumoutsakos P (2002). A parameter study for differential evolution. In: Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, WSEAS Press,
  14. Interlaken, Switzerland, pp. 293-298.
  15. Gandomi AH, Yang XS (2012). Evolutionary boundary constraint handling scheme. Neural