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Network Modeling of Fin-and-Tube Evaporator Performance Under Dry and Wet Conditions

机译：干湿条件下翅片管蒸发器性能的网络建模

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摘要

A new neural network modeling approach to the evaporator performance under dry and wet conditions has been developed. Not only the total cooling capacity but also the sensible heat ratio and pressure drops on both air and refrigerant sides are modeled. Since the evaporator performance under dry and wet conditions is, respectively, dominated by the dry-bulb temperature and the web-bulb temperature, two neural networks are used together for capturing the characteristics. Training of a multi-input multi-output neural network is separated into training of multi-input single-output neural networks for improving the modeling flexibility and training efficiency. Compared with a well-developed physics-based model, the standard deviations of trained neural networks under dry and wet conditions are less than 1% and 2%, respectively. Compared with the experimental data, errors fall into ±5%.
机译：已经开发了一种新的神经网络建模方法，用于干燥和潮湿条件下的蒸发器性能。不仅对总冷却能力，而且对空气和制冷剂侧的显热比和压降进行了建模。由于干和湿条件下蒸发器的性能分别由干球温度和腹板球温度决定，因此两个神经网络一起用于捕获特性。多输入多输出神经网络的训练被分为多输入单输出神经网络的训练，以提高建模的灵活性和训练效率。与成熟的基于物理的模型相比，经过训练的神经网络在干燥和潮湿条件下的标准偏差分别小于1％和2％。与实验数据相比，误差降至±5％。

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  • 来源
    《Journal of Heat Transfer》 |2010年第7期|P.074502.1-074502.4|共4页
  • 作者

    Ling-Xiao Zhao; Liang Yang; Chun-Lu Zhang;

  • 作者单位

    Institute of Refrigeration and Cryogenics,Shanghai Jiaotong University,Shanghai 200240, China;

    rnInstitute of Refrigeration and Cryogenics,Shanghai Jiaotong University,Shanghai 200240, China China R&D Center,Carrier Corporation,No. 3239 Shen Jiang Road,Shanghai 201206, China;

    rnFaculty of Mechanical Engineering,Tongji University,No. 4800 Cao An Road,Shanghai 201804, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    heat exchanger; evaporator; model; neural network;

    机译：热交换器;蒸发器模型;神经网络;

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