0
Technical Briefs

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

[+] Author and Article Information
Ling-Xiao Zhao

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

Liang Yang

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

Chun-Lu Zhang1

Faculty of Mechanical Engineering, Tongji University, No. 4800 Cao An Road, Shanghai 201804, Chinachunlu.zhang@gmail.com

1

Corresponding author.

J. Heat Transfer 132(7), 074502 (May 05, 2010) (4 pages) doi:10.1115/1.4000950 History: Received December 25, 2008; Revised December 01, 2009; Published May 05, 2010; Online May 05, 2010

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%.

FIGURES IN THIS ARTICLE
<>
Copyright © 2010 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

Architecture of three-layer perceptron network

Grahic Jump Location
Figure 2

Constant total cooling capacity lines under different working conditions

Grahic Jump Location
Figure 3

Standard deviation of the total cooling capacity under wet conditions versus number of hidden neurons

Grahic Jump Location
Figure 4

Model validation of trained neural network with lab test data

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In