基于熵增生成数的肋式换热器神经遗传优化

IF 0.8 Q4 THERMODYNAMICS

Archives of Thermodynamics Pub Date : 2023-07-20 DOI:10.24425/ather.2020.133627

P. K. Konchada

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引用次数: 1

摘要

本文对带肋的方形管道层流条件下的换热进行了数值模拟。肋以交错的方式安装在方形管道的顶部和底部墙壁上。雷诺数在200到600之间变化。研究了不同长度、宽度和深度的肋结构对传热、摩擦系数和熵增生成数的影响。进一步利用人工神经网络结合遗传算法，在选定范围内选取最优肋尺寸，实现熵增生成数(性能因子)的最小化。将遗传算法与微遗传算法进行了比较，考察了遗传算法在求解精度方面所节省的计算时间。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Neuro-genetic optimization of ribbed heat exchanger using entropy augmentation generation number

Numerical predictions of heat transfer under laminar conditions in a square duct with ribs are presented in this paper. Ribs are provided on top and bottom walls in a square duct in a staggered manner. The flow rates have been varied between Reynolds number 200 and 600. Various configurations of ribs by varying length, width and depth have been investigated for their effect on heat transfer, friction factor and entropy augmentation generation number. Further artificial neural network integrated with genetic algorithm was used to minimize the entropy augmentation generation number (performance factor) by selecting the optimum rib dimensions in a selected range. Genetic algorithm is compared with microgenetic algorithm to examine the reduction in computational time for outlay of solution accuracy.

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来源期刊

Archives of Thermodynamics THERMODYNAMICS-

CiteScore

1.80

自引率

22.20%

发文量

期刊介绍： The aim of the Archives of Thermodynamics is to disseminate knowledge between scientists and engineers interested in thermodynamics and heat transfer and to provide a forum for original research conducted in Central and Eastern Europe, as well as all over the world. The journal encompass all aspect of the field, ranging from classical thermodynamics, through conduction heat transfer to thermodynamic aspects of multiphase flow. Both theoretical and applied contributions are welcome. Only original papers written in English are consider for publication.