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锯齿形板翅式换热器热力学特性的优化分析。

Optimization Analysis of Thermodynamic Characteristics of Serrated Plate-Fin Heat Exchanger.

机构信息

School of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China.

出版信息

Sensors (Basel). 2023 Apr 21;23(8):4158. doi: 10.3390/s23084158.

DOI:10.3390/s23084158
PMID:37112498
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10146975/
Abstract

This study explores the use of Multi-Objective Genetic Algorithm (MOGA) for thermodynamic characteristics of serrated plate-fin heat exchanger (PFHE) under numerical simulation method. Numerical investigations on the important structural parameters of the serrated fin and the factor and the factor of PFHE are conducted, and the experimental correlations about the factor and the factor are determined by comparing the simulation results with the experimental data. Meanwhile, based on the principle of minimum entropy generation, the thermodynamic analysis of the heat exchanger is investigated, and the optimization calculation is carried out by MOGA. The comparison results between optimized structure and original show that the factor increases by 3.7%, the factor decreases by 7.8%, and the entropy generation number decreases by 31%. From the data point of view, the optimized structure has the most obvious effect on the entropy generation number, which shows that the entropy generation number can be more sensitive to the irreversible changes caused by the structural parameters, and at the same time, the factor is appropriately increased.

摘要

本研究探讨了多目标遗传算法（MOGA）在锯齿形板翅式换热器（PFHE）热力学特性数值模拟方法中的应用。对锯齿形翅片的重要结构参数和因子以及 PFHE 的因子进行了数值研究，并通过将模拟结果与实验数据进行比较，确定了因子和因子的实验关联式。同时，基于最小熵产生原理，对换热器的热力学进行了分析，并采用 MOGA 进行了优化计算。优化结构与原始结构的比较结果表明，因子增加了 3.7%，因子降低了 7.8%，熵产生数降低了 31%。从数据角度来看，优化结构对熵产生数的影响最为明显，这表明熵产生数对结构参数引起的不可逆变化更为敏感，同时适当增加了因子。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/f1fc326ee89b/sensors-23-04158-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/045aa2c7b2a1/sensors-23-04158-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/bdb5c1e780e8/sensors-23-04158-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/44b5624fd161/sensors-23-04158-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/7159a5751630/sensors-23-04158-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/202a8dcf0d68/sensors-23-04158-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/f1fc326ee89b/sensors-23-04158-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/5348446dbc3e/sensors-23-04158-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/84017792c973/sensors-23-04158-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/3aa65ea5c650/sensors-23-04158-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/9d8c79df8813/sensors-23-04158-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/d22af8135ec6/sensors-23-04158-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/6e73c176ef25/sensors-23-04158-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/045aa2c7b2a1/sensors-23-04158-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/bdb5c1e780e8/sensors-23-04158-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/44b5624fd161/sensors-23-04158-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/7159a5751630/sensors-23-04158-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/202a8dcf0d68/sensors-23-04158-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe74/10146975/f1fc326ee89b/sensors-23-04158-g012.jpg

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