A Leakage Detection Method for a Vapor-Compression Refrigeration System Based on System Modeling

Gao Zanjun; Jiang Ning; Kong Jiaqi; Lu Yongjie; Yang Jialiang; Yan Yuhao; Han Xiaohong

doi:10.12465/issn.0253-4339.20250822001

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A Leakage Detection Method for a Vapor-Compression Refrigeration System Based on System Modeling

更新时间：2026-05-06

- A Leakage Detection Method for a Vapor-Compression Refrigeration System Based on System Modeling
- Journal of Refrigeration Pages: 1-9(2026)
- 作者机构：
  
  1.中国航空工业集团公司金城南京机电液压工程研究中心南京 211106
  2. 航空机电系统综合航空科技重点实验室南京 211106
  3. 浙江大学制冷与低温研究所全省制冷与低温技术重点实验室杭州 310027
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(52076185)
- DOI：10.12465/issn.0253-4339.20250822001
  CLC： TB657;TP277
- Received：22 August 2025，
  
  Revised：2026-03-30，
  
  Accepted：30 March 2026，
  
  Online First：06 May 2026，
- 稿件说明：
移动端阅览
高赞军,江宁,孔嘉麒等.基于系统建模的蒸汽压缩制冷系统泄漏诊断方法[J].制冷学报,

Gao Zanjun,Jiang Ning,Kong Jiaqi,et al.A Leakage Detection Method for a Vapor-Compression Refrigeration System Based on System Modeling[J].Journal of Refrigeration,
高赞军,江宁,孔嘉麒等.基于系统建模的蒸汽压缩制冷系统泄漏诊断方法[J].制冷学报, DOI：10.12465/issn.0253-4339.20250822001.

Gao Zanjun,Jiang Ning,Kong Jiaqi,et al.A Leakage Detection Method for a Vapor-Compression Refrigeration System Based on System Modeling[J].Journal of Refrigeration, DOI：10.12465/issn.0253-4339.20250822001.

摘要

制冷剂泄漏不仅导致系统性能衰减、能耗上升，更因其高GWP（全球变暖潜值，Global Warming Potential）或可燃性而加剧环境危害与安全风险，亟需发展快速、准确的泄漏检测与诊断方法。本文针对制冷量3.5 kW的家用空调制冷系统，采用模拟仿真方法，定量研究了不同泄漏率下系统各部件关键节点参数及系统性能参数的动态演变规律。在此基础上，提出并构建了一套基于系统建模的制冷剂泄漏检测与诊断方法。该方法旨在利用少量泄漏数据，实现对泄漏状态的程度评估，建立的制冷剂充注存量预测方程，在泄漏率低于12%时，预测误差保持在±5%以内。

Abstract

Refrigerant leakage from a refrigeration system not only degrades the system performance and increases energy consumption but also aggravates environmental hazards and safety risks because of the high global warming potential or flammability of many refrigerants. Therefore， fast and accurate methods are required to detect and rectify refrigerant leaks. This study focused on a domestic air-conditioning refrigeration system with a cooling capacity of 3.5 kW. Using simulation methods， the dynamic evolution laws of the key node parameters of each component and the system performance parameters under different leakage rates were analyzed quantitatively. Based on the findings， a refrigerant inventory prediction model was developed for the key components. This model， integrated with the operational data， provides a new leakage detection and diagnosis method based on system modeling. This method aims to evaluate the severity of leakage using a small amount of leakage data. The developed refrigerant charge inventory prediction equation maintains the prediction error within ±5% when the leakage rate is below 12%.

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references

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