Unified physics-informed ResNet model for comprehensive performance prediction of finned-tube evaporators

IF 3.5 2区 工程技术 Q1 ENGINEERING, MECHANICAL
Bo Zhang, Xing-Yu Liang, Chun-Lu Zhang
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引用次数: 0

Abstract

Finned-tube heat exchangers are widely used in refrigeration and thermal engineering. With the growing demand for digital twins in complex systems, the need for fast and accurate performance prediction has become more urgent. Existing neural network models often suffer from fragmented structures, incomplete parameter considerations, and an inability to capture essential physical details. This study presents a unified physics-informed residual network model that overcomes these limitations by ensuring parameter completeness and integrating physical principles into the network architecture. The proposed method avoids redundant input parameters and conforms to physical laws. Moreover, it ensures that the selected output parameters meet the requirements of actual performance prediction and are conducive to the training of neural networks. By combining residual blocks with independent layers, the model achieves joint prediction of multiple performance metrics, improving both efficiency and accuracy. The proposed model significantly enhances predictive performance. All the determination coefficients reach 0.999, while both the mean absolute error and root mean square error values remain remarkably low. Specifically, it achieves a mean absolute percentage error of 0.41% for total heat transfer rate, 0.65% for sensible heat transfer rate, and below 0.3% for both refrigerant and air pressure drop. Furthermore, it effectively captures critical physical details, such as transitions between dry and wet operating conditions. Compared to previous models, this approach provides a more comprehensive, physically consistent, and computationally efficient framework for finned-tube evaporator performance prediction.
用于翅片管蒸发器综合性能预测的统一物理通知ResNet模型
翅片管换热器在制冷和热力工程中有着广泛的应用。随着复杂系统中对数字孪生的需求不断增长,对快速准确的性能预测的需求变得更加迫切。现有的神经网络模型往往存在结构碎片化、参数考虑不完整以及无法捕捉基本物理细节的问题。本研究提出了一个统一的物理信息剩余网络模型,通过确保参数完整性和将物理原理集成到网络架构中来克服这些限制。该方法避免了输入参数冗余,符合物理规律。并且保证了所选择的输出参数满足实际性能预测的要求,有利于神经网络的训练。通过将残差块与独立层相结合,实现了多个性能指标的联合预测,提高了效率和准确性。该模型显著提高了预测性能。所有决定系数均达到0.999,平均绝对误差和均方根误差值都很低。具体而言,总传热率的平均绝对百分比误差为0.41%,显热传热率的平均绝对百分比误差为0.65%,制冷剂和空气压降的平均绝对百分比误差均低于0.3%。此外,它还能有效地捕捉关键的物理细节,例如干燥和潮湿操作条件之间的转换。与以前的模型相比,该方法为翅片管蒸发器性能预测提供了更全面、物理上一致和计算效率更高的框架。
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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来源期刊
CiteScore
7.30
自引率
12.80%
发文量
363
审稿时长
3.7 months
期刊介绍: The International Journal of Refrigeration is published for the International Institute of Refrigeration (IIR) by Elsevier. It is essential reading for all those wishing to keep abreast of research and industrial news in refrigeration, air conditioning and associated fields. This is particularly important in these times of rapid introduction of alternative refrigerants and the emergence of new technology. The journal has published special issues on alternative refrigerants and novel topics in the field of boiling, condensation, heat pumps, food refrigeration, carbon dioxide, ammonia, hydrocarbons, magnetic refrigeration at room temperature, sorptive cooling, phase change materials and slurries, ejector technology, compressors, and solar cooling. As well as original research papers the International Journal of Refrigeration also includes review articles, papers presented at IIR conferences, short reports and letters describing preliminary results and experimental details, and letters to the Editor on recent areas of discussion and controversy. Other features include forthcoming events, conference reports and book reviews. Papers are published in either English or French with the IIR news section in both languages.
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