4.7 Article

Optimization investigation on configuration parameters of serrated fin in plate-fin heat exchanger based on fluid structure interaction analysis

期刊

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijheatmasstransfer.2017.11.058

关键词

Plate-fin heat exchanger; Serrated fin; Stress analysis; Response surface; Analysis of variance; Multi-Objective Genetic Algorithm

资金

  1. National Natural Science Foundation of China [51676146]
  2. Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Tsinghua University [ARES201701]

向作者/读者索取更多资源

The comprehensive performance of serrated fin in plate-fin heat exchangers (PFHEs) is numerically studied based on fluid structure interaction (FSI) analysis in this paper. Based on Full 2nd-Order Polynomial response surface (RS) and analysis of variance (ANOVA), the effects and second order interaction effects of the fin height, fin space, fin thickness and fin interrupted length on heat transfer, flow resistance and stress are quantitatively and thoroughly assessed. The results show that the heat transfer is the most sensitive to fin interrupted length, and the flow resistance and the maximum stress are the most sensitive to fin thickness. The interaction effect of the fin space and fin interrupted length is the strongest when the j factor is set as objective function, while the interaction effect of the fin space and fin thickness is the strongest when the f factor or maximum stress is set as objective function. Based on Full 2nd-Order Polynomial RS, Multi-Objective Genetic Algorithm (MOGA) is applied to optimize the fin structure comprehensively, with enhancing heat transfer, decreasing pressure drop and stress set as objectives. To demonstrate the effectiveness of optimized structures, a comprasion between the original design and optimized structures is performed. The results show that the JF factor of optimized structures 2 and 3 increases by 23.0% and 19.7% respectively, and the maximum stress decreases by 5.8% and 15.2% respectively. The MOGA offers theorectical guidance for optimization design of PFHE. (C) 2017 Elsevier Ltd. All rights reserved.

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