• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.4
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• pp.230-237
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• 2008

The objective of this paper is to investigate the performance characteristics of a $CO_2$ cooling and water heating system using a twin-rotary compressor with the compression volume ratio of 0.6. The cooling performances of the $CO_2$ heat pump were measured and analyzed with the variations of charge amount, EEV opening, and compressor frequency. In addition, the performance of the combined system including cooling and water heating was also measured and analyzed by varying inlet temperature of the EEV. As a result, the optimal normalized charge and cooling COP in the cooling mode were 0.307 and 2.06, respectively. The application of the water heating into the $CO_2$ heat pump improved the cooling performance over 78% and decreased the EEV inlet temperature by $8^{\circ}C$, which can increase system reliability.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.31 no.3
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• pp.19-22
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• 2002

• Proceedings of the SAREK Conference
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• 2006.11a
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• pp.85-85
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• 2006

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.358-363
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• 2007

In this study, experimental study on the heating performance of a $CO_2$ heat pump with gas injection was performed varying gas injection ratio and outdoor temperature to improve the heating performance of $CO_2$ heat pump. The twin rotary compressor having volume ratio of 0.7 was adopted in the $CO_2$ heat pump. From the test results, the heating capacity and COP were increased and the compressor discharge temperature was decreased with the increase of injection ratio. At the outdoor temperature of $-8^{\circ}C$, the heating capacity and COP with the injection were increased by 45% and 24%, respectively, compared with non-injection condition.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.