Share

IIR document

Development of an automated compressor performance mapping using artificial neural network and multiple compressor technologies.

Author(s) : MA J., DING X., HORTON W. T., ZIVIANI D.

Type of article: IJR article

Summary

In the last decades, several technological improvements to positive displacement compressors have been developed and introduced into market. During the process of implementing new compressor technologies, high-accuracy numerical models are essential to predict the performance at both component and system levels. ANSI/AHRI Standard 540 10-coefficient cubic polynomial model is still the industry-standard compressor mapping approach despite the well documented limitations. In order to generate accurate compressor maps by using the 10-coefficient cubic polynomial models, compressor manufacturers are required to obtain, in some cases, more than 20 compressor-calorimeter data points depending on the compressor type and operating envelope. This paper attempts to address the need for more generalized and versatile compressor mapping methodologies as well as to reduce the time-consuming and expensive compressor calorimeter testing. To this end, an automated compressor performance mapping approach based on artificial neural network (ANN) is proposed to identify the compressor operating envelope and map the performance of any positive displacement compressors for HVAC&R applications with minimum number of data points. In addition, the paper also aims at demonstrating the feasibility and reliability of the proposed automated compressor performance mapping approach for training the ANN models in comparison to conventional 10-coefficient cubic polynomial maps. Three different compressor types, i.e. reciprocating, scroll, and rotary rolling piston, have been considered as test cases.

Available documents

Format PDF

Pages: 66-80

Available

  • Public price

    20 €

  • Member price*

    Free

* Best rate depending on membership category (see the detailed benefits of individual and corporate memberships).

Details

  • Original title: Development of an automated compressor performance mapping using artificial neural network and multiple compressor technologies.
  • Record ID : 30027840
  • Languages: English
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 120
  • Publication date: 2020/12
  • DOI: http://dx.doi.org/10.1016/j.ijrefrig.2020.08.001
  • Document available for consultation in the library of the IIR headquarters only.

Links


See other articles in this issue (42)
See the source

Indexing