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Research Papers: Heat Exchangers

Optimal Arrangement Design of a Tube Bundle in Cross-Flow Using Computational Fluid Dynamics and Multi-Objective Genetic Algorithm

[+] Author and Article Information
Ya Ge, Feng Xin, Wei Liu

School of Energy and Power Engineering,
Huazhong University of Science and Technology,
Wuhan 430074, China

Yao Pan

The China Academy of Launch Vehicle
Technology,
Beijing 100076, China

Zhichun Liu

School of Energy and Power Engineering,
Huazhong University of Science and Technology,
Wuhan 430074, China
e-mail: zcliu@hust.edu.cn

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received November 23, 2018; final manuscript received April 11, 2019; published online May 14, 2019. Assoc. Editor: Danesh K. Tafti.

J. Heat Transfer 141(7), 071801 (May 14, 2019) (9 pages) Paper No: HT-18-1766; doi: 10.1115/1.4043570 History: Received November 23, 2018; Revised April 11, 2019

Recently, energy saving problem attracts increasing attention from researchers. This study aims to determine the optimal arrangement of a tube bundle to achieve the best overall performance. The multi-objective genetic algorithm (MOGA) is employed to determine the best configuration, where two objective functions, the average heat flux q and the pressure drop Δp, are selected to evaluate the performance and the consumption, respectively. Subsequently, a decision maker method, technique for order preference by similarity to an ideal solution (TOPSIS), is applied to determine the best compromise solution from noninferior solutions (Pareto solutions). In the optimization procedure, all the two-dimensional (2D) symmetric models are solved by the computational fluid dynamics (CFD) method. Results show that performances alter significantly as geometries of the tube bundle changes along the Pareto front. For the case 1 (using staggered arrangement as initial), the optimal q varies from 2708.27 W/m2 to 3641.25 W/m2 and the optimal Δp varies from 380.32 Pa to 1117.74 Pa, respectively. For the case 2 (using in-line arrangement as initial), the optimal q varies from 2047.56 W/m2 to 3217.22 W/m2 and the optimal Δp varies from 181.13 Pa to 674.21 Pa, respectively. Meanwhile, the comparison between the optimal solution with maximum q and the one selected by TOPSIS indicates that TOPSIS could reduce the pressure drop of the tube bundle without sacrificing too much heat transfer performance.

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Figures

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Fig. 1

Schematic of the forced convective heat transfer cases. (a) Cases 1, staggered arrangement as initial case and (b) cases 2, in-line arrangement as initial case.

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Fig. 2

Flowchart of the optimization procedure

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Fig. 3

The average Nu and Δp calculated by different DOF numbers with Re = 13,000 for the case 1

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Fig. 4

Grid systems for calculation cases

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Fig. 5

Pareto fronts obtained by the multi-objective genetic algorithm

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Fig. 6

Velocity comparison between two different cases around the turning point. (a) Case 1, (J1, J2) = (−2804.4, 393.68) and (b) case 2, (J1, J2) = (−2791.5, 393.10).

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Fig. 7

Velocity fields of four optimal solutions selected from the Pareto front

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Fig. 8

Temperature fields of four optimal solutions selected from the Pareto front

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Fig. 9

Ranking results of two Pareto fronts

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Fig. 10

Comparison of the velocity field. (a) The best solution determined by TOPSIS. (b) The initial in-line arrangement.

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Fig. 11

Comparison of the temperature field. (a) The best solution determined by TOPSIS. (b) The initial in-line arrangement.

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Fig. 12

Velocity vectors around the tubes C7, 2 and C8, 2. (a) The best solution determined by TOPSIS. (b) The initial in-line arrangement.

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Fig. 13

Average heat flux of front half, rear half for each row of tubes

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Fig. 14

Performances of optimal tube bundles under different evaluation criteria

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