• Proceedings of the SAREK Conference
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• 2003.07a
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• pp.123-123
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• 2003

• Proceedings of the SAREK Conference
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• 2004.06a
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• pp.137-137
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• 2004

• Proceedings of the SAREK Conference
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• 2006.11a
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• pp.44-44
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• 2006

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

• Transactions of the Korean Society of Mechanical Engineers B
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• v.32 no.6
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• pp.454-462
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• 2008

Since operating conditions are significantly different for heating and cooling mode operations in a $CO_2$ heat pump system, it is difficult to optimize the performance of the $CO_2$ cycle. In addition, the performance of a $CO_2$ heat pump is very sensitive to outdoor temperature and gascooler pressure. In this study, the cooling and heating performances of a variable speed $CO_2$ heat pump with a twin-rotary compressor were measured and analyzed with the variations of EEV opening and compressor frequency. As a result, the cooling and heating COPs were 2.3 and 3.0, respectively, when the EEV opening was 22%. When the optimal EEV openings for heating and cooling were 28% and 16%, the cooling and heating COPs increased by 3.3% and 3.9%, respectively, over the COPs at the EEV opening of 22%. Beside, the heating performance was more sensitive to EEV opening than the cooling performance. As the compressor speed decreased by 5 Hz, the cooling COP increased by 2%, while the heating COP decreased by 8%.

• International Journal of Air-Conditioning and Refrigeration
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• v.16 no.3
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• pp.77-82
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• 2008

For heat pumps used in a cold region, it is very important to obtain appropriate heating capacity. Several studies using a variable speed compressor and an additional heater have been performed to enhance heating capacity at low ambient temperatures. However, for outdoor temperature conditions below $-15^{\circ}C$, it is still difficult to obtain enough heating capacity above the rated value. In recent studies, the application of gas injection technique into a two-stage heat pump yielded noticeable heating performance improvement at low temperature conditions. In this study, the heating performance of a two-stage gas injection heat pump with a rated capacity of 3.5 kW was measured and analyzed by varying refrigerant charge amount and EEV opening at the standard heating condition. The heating performance of the two-stage gas injection heat pump was compared with that of a two-stage non-injection heat pump. The heating capacity and COP of the two-stage gas injection heat pump were improved by 2-10% at the optimal charging condition over those of the two-stage non-injection heat pump.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.21 no.8
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• pp.425-432
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• 2009

In this study, the improvement of cooling capacity by applying gas injection technique in a two-stage heat pump using R410A was experimentally investigated. A twin rotary type compressor with gas injection was applied to the heat pump system. The optimum refrigerant charge for the injection and the non-injection cycles was selected to achieve the maximum COP at the cooling standard condition. The injection cycle showed less optimum refrigerant charge than that of the non-injection cycle. The cooling performances of the injection and the non-injection cycles were measured and compared by varying compressor frequency from 40 to 90 Hz. The cooling capacity of the gas injection cycle was 1.6% -11.3% higher than that of the non-injection cycle. The COP of the gas injection cycle was 13.7% to 28.9% higher than that of the non-injection cycle at the same cooling capacity. The heat pump system showed stable operation after 30% of the injection valve opening.

• 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.