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PDSim: a generalized modeling platform to predict the performance of positive displacement compressors and expanders.

Number: pap. F30

Author(s) : ZIVIANI D., ZHANG X., BRAUN J. E., et al.

Summary

Detailed and comprehensive simulation models of hermetic, semi-hermetic and open-drive positive displacement compressors, including scroll, rotary, reciprocating, spool, Bowtie, Z-compressor, and S-RAM among others, have been developed throughout the years and validated against experimental compressor measurements. These models were also used to perform parametric studies where the influences of compressor geometry, leakage gaps, heat transfer coefficients and frictional coefficients on performance were investigated. This paper describes a new comprehensive positive displacement simulation platform based on the previous compressor modeling efforts to predict the performance of hermetic positive displacement compressors. The platform will include all of the main aspects of compressor modeling, such as the geometry (a library of multiple positive displacement compressor geometries is being programmed), the thermodynamic governing equations, i.e., energy and mass balances for the compression process, internal leakage paths, suction and discharge valve dynamics, internal heat transfer, friction and mechanical losses as well as electric motor losses. The generic model platform has been validated using external compressor performance measurements, which are readily available from the previous testing efforts with various compressors and refrigerants using hot gas-bypass compressor load stands and a calorimeter. The ability of the PDSim modeling platform to accurately predict the performance of hermetic positive displacement compressors will be discussed in details.

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Details

  • Original title: PDSim: a generalized modeling platform to predict the performance of positive displacement compressors and expanders.
  • Record ID : 30022132
  • Languages: English
  • Source: 8th international conference on compressors and refrigeration, 2017.
  • Publication date: 2017/07/20

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