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

摘要

The comprehensive performance of sine wavy fin in plate-fin heat exchangers (PFHEs) is numerically studied based on fluid structure interaction (FSI) analysis in this paper. The analysis results of stress distribution reveal that the highest stress is located in the inlet and outlet region of fin structure, and the fluctuant stress reaches to the peak in the wave crest. By way of analyzing Full 2nd-Order Polynomial response surface (RS), the effects of inlet velocity and five configuration parameters (fin height, fin space, fin thickness, fin wavelength and double amplitude) on heat transfer, flow resistance and stress of sine wavy fin structure are quantitatively assessed. The results reveal that the j factor increases with the increase of fin space and fin height, and decreases with the increase of fin thickness, wavelength and inlet velocity. The j factor firstly increases with the increase of double amplitude and then decreases. The f factor increases with double amplitude, fin space and fin height, and decreases with fin thickness, wavelength and inlet velocity. The maximum stress increases with the increase of wavelength and fin space, and decreases with the increase of fin thickness and double amplitude. The interaction effects of input parameters on the j factor and f factor are not obvious. While the interaction effects of fin thickness and wavelength, double amplitude and wavelength on the maximum stress are obvious. Based on RS, Multi-objective Genetic Algorithm (MOGA) is performed to optimize the fin structure comprehensively, with multiple objectives of increasing the JF factor (JF = j/f(1/3)) and decreasing the maximum stress to the best. The optimization results shows that, compared with the original design, the JF factor of optimal design 1, 2, and 3 increases by 11.0%, 8.4% and 15.9% respectively, and the maximum stress decreases by 32.3%, 42.4% and 20.7% respectively. (C) 2018 Elsevier Ltd. All rights reserved.