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Research Papers: Energy Systems Analysis

Multi-Objective Optimization of a Steam Surface Condenser Using the Territorial Particle Swarm Technique

[+] Author and Article Information
Pooya Mirzabeygi

Department of Mechanical and
Materials Engineering,
The University of Western Ontario,
London, ON N6A 5B9, Canada
e-mail: pmirzabe@uwo.ca

Chao Zhang

Mem. ASME
Department of Mechanical and
Materials Engineering,
The University of Western Ontario,
London, ON N6A 5B9, Canada
e-mail: czhang@eng.uwo.ca

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received August 3, 2014; final manuscript received January 10, 2016; published online March 10, 2016. Assoc. Editor: Abel Hernandez-Guerrero.

J. Energy Resour. Technol 138(5), 052001 (Mar 10, 2016) (10 pages) Paper No: JERT-14-1238; doi: 10.1115/1.4032727 History: Received August 03, 2014; Revised January 10, 2016

The multi-objective territorial particle swarm optimization (MOTPSO) technique is proposed in this work for the optimal design of steam surface condensers. The main objective of this work is to maximize the condensation rate in a condenser while the pressure loss is minimized. Various design parameters, such as the tube outside diameter, thickness, and pitch, are considered to find the optimal ones for shell and tube heat exchangers considered in this study. The two-dimensional computational fluid dynamics (CFD) analysis is performed to solve the fluid flow and heat transfer in the condenser to assess the performance of different designs.

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References

Figures

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Fig. 1

Nearest neighbor density estimator quality criterion for the leader selection from the archive [34]

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Fig. 2

Global best, personal best, and external archive concepts for two minimum objectives [34]

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Fig. 3

Optimization procedure

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Fig. 4

Configuration of the experimental condenser

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Fig. 5

Two-dimensional mesh generated for condenser simulations

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Fig. 6

Validation of the CFD model by comparing the predicted heat flux with the experimental data: (a) 3rd row tubes, (b) 8th row tubes, (c) 13th row tubes, and (d) 18th row tubes from the bottom of the tube bundle

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Fig. 7

Set of nondominated solution, Pareto front, obtained by the algorithm

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Fig. 8

Contours of the pressure for the selected design candidates: (a) highest condensation rate and (b) lowest pressure drop

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Fig. 9

Contours of the condensation rate for the selected design candidates: (a) highest condensation rate and (b) lowest pressure drop

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Fig. 10

Contours of the pressure and condensation rate for the preferred design: (a) pressure and (b) condensation rate

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