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A critical review and comparative study on positive displacement compressor models for fast performance prediction in wide working condition ranges.

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Title: A critical review and comparative study on positive displacement compressor models for fast performance prediction in wide working condition ranges.
Authors: Wang, Longyan1 (AUTHOR), Cao, Haomin1 (AUTHOR), Ding, GuoLiang1 (AUTHOR) glding@sjtu.edu.cn, Shao, Yanpo2 (AUTHOR), Yue, Bao3 (AUTHOR), Wu, Zhigang3 (AUTHOR), Liao, Jiansheng4 (AUTHOR)
Source: Science & Technology for the Built Environment. Mar2026, Vol. 32 Issue 3, p330-354. 25p.
Subjects: Compressor performance, Compressors, Computer simulation, Extrapolation, Refrigeration & refrigerating machinery, Mathematical models, Model validation, Statistical models
Abstract: Positive displacement compressors, e.g., reciprocating, rotary, and scroll compressors, are commonly employed in vapor compression refrigeration systems using various refrigerants, and they may operate under a wide range of working conditions. In the simulation-based design of refrigeration systems, a suitable compressor model for fast and stable prediction of compressor performance is extremely important. This study aims to investigate the applicability of existing models in wide working condition ranges, and to recommend suitable ones which could simultaneously satisfy the following three requirements: high accuracy, reliable extrapolation capability, and small amount of data required for model calibration. The most representative compressor models, including eight data-driven models and four semi-empirical ones, are selected from publications, and they are validated using data from experiments conducted by the present authors as well as those from publications. Model validation results show that the top-performing data-driven and semi-empirical models can achieve sufficient accuracy with an average deviation of less than 2%; compared to data-driven models, semi-empirical models can better maintain accuracy and reasonable trends when extrapolating, and require fewer calibration data to achieve the same level of accuracy. In conclusion, among the existing fast performance-prediction compressor models, semi-empirical ones should be preferentially recommended. [ABSTRACT FROM AUTHOR]
Copyright of Science & Technology for the Built Environment is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 192207237
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: A critical review and comparative study on positive displacement compressor models for fast performance prediction in wide working condition ranges.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Longyan%22">Wang, Longyan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Haomin%22">Cao, Haomin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ding%2C+GuoLiang%22">Ding, GuoLiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> glding@sjtu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Shao%2C+Yanpo%22">Shao, Yanpo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yue%2C+Bao%22">Yue, Bao</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Zhigang%22">Wu, Zhigang</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liao%2C+Jiansheng%22">Liao, Jiansheng</searchLink><relatesTo>4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Science+%26+Technology+for+the+Built+Environment%22">Science & Technology for the Built Environment</searchLink>. Mar2026, Vol. 32 Issue 3, p330-354. 25p.
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  Data: <searchLink fieldCode="DE" term="%22Compressor+performance%22">Compressor performance</searchLink><br /><searchLink fieldCode="DE" term="%22Compressors%22">Compressors</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Extrapolation%22">Extrapolation</searchLink><br /><searchLink fieldCode="DE" term="%22Refrigeration+%26+refrigerating+machinery%22">Refrigeration & refrigerating machinery</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Positive displacement compressors, e.g., reciprocating, rotary, and scroll compressors, are commonly employed in vapor compression refrigeration systems using various refrigerants, and they may operate under a wide range of working conditions. In the simulation-based design of refrigeration systems, a suitable compressor model for fast and stable prediction of compressor performance is extremely important. This study aims to investigate the applicability of existing models in wide working condition ranges, and to recommend suitable ones which could simultaneously satisfy the following three requirements: high accuracy, reliable extrapolation capability, and small amount of data required for model calibration. The most representative compressor models, including eight data-driven models and four semi-empirical ones, are selected from publications, and they are validated using data from experiments conducted by the present authors as well as those from publications. Model validation results show that the top-performing data-driven and semi-empirical models can achieve sufficient accuracy with an average deviation of less than 2%; compared to data-driven models, semi-empirical models can better maintain accuracy and reasonable trends when extrapolating, and require fewer calibration data to achieve the same level of accuracy. In conclusion, among the existing fast performance-prediction compressor models, semi-empirical ones should be preferentially recommended. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Science & Technology for the Built Environment is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/23744731.2026.2613623
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      – Code: eng
        Text: English
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        PageCount: 25
        StartPage: 330
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      – SubjectFull: Compressor performance
        Type: general
      – SubjectFull: Compressors
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Extrapolation
        Type: general
      – SubjectFull: Refrigeration & refrigerating machinery
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      – SubjectFull: Mathematical models
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      – SubjectFull: Model validation
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      – SubjectFull: Statistical models
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      – TitleFull: A critical review and comparative study on positive displacement compressor models for fast performance prediction in wide working condition ranges.
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            NameFull: Wang, Longyan
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            NameFull: Cao, Haomin
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            NameFull: Ding, GuoLiang
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            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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