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dc.contributor.authorGe, Ya
dc.contributor.authorXin, Feng
dc.contributor.authorPan, Yao
dc.contributor.authorLiu, Zhichun
dc.contributor.authorLiu, Wei
dc.date.accessioned2019-09-18T09:00:58Z
dc.date.available2019-09-18T09:00:58Z
dc.date.copyright5/14/2019 12:00:00 AM
dc.date.issued2019
dc.identifier.issn0022-1481
dc.identifier.otherht_141_07_071801
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4257902
dc.description.abstractRecently, 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.
dc.publisherAmerican Society of Mechanical Engineers (ASME)
dc.titleOptimal Arrangement Design of a Tube Bundle in Cross-Flow Using Computational Fluid Dynamics and Multi-Objective Genetic Algorithm
dc.typeJournal Paper
dc.journal.volume141
dc.journal.issue7
dc.journal.titleJournal of Heat Transfer
dc.identifier.doi10.1115/1.4043570
dc.journal.fristpage71801
dc.journal.lastpage071801-9
dc.treeJournal of Heat Transfer:;2019:;volume( 141 ):;issue: 007
dc.contenttypeFulltext


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