The performances of a refrigeration unit relying on compressors working in parallel have been investigated considering the influence of the compressor volumetric efficiency and isentropic efficiency on the compression...The performances of a refrigeration unit relying on compressors working in parallel have been investigated considering the influence of the compressor volumetric efficiency and isentropic efficiency on the compression ratio.Moreover,the following influential factors have been taken into account:evaporation temperature,condensation temperature and compressor suction-exhaust pressure ratio for different opening conditions of the compressor.The following quantities have been selected as the unit performance measurement indicators:refrigeration capacity,energy efficiency ratio(COP),compressor power consumption,and refrigerant flow rate.The experimental results indicate that the system refrigeration capacity and COP decrease with a decrease in evaporation temperature,increase of condensation temperature,and increase in pressure ratio.The refrigerant flow rate increases with the increase in evaporation temperature,decrease in condensing temperature and increase in pressure ratio.The compressor power consumption increases with the increase in condensing temperature and increase in pressure ratio,but is not significantly affected by the evaporation temperature.展开更多
The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain ti...The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.展开更多
基金supported by the National Natural Science Foundation of China(No.41877251)the Key project of Natural Science Foundation of Tianjin City(No.6JCZDJC39000).
文摘The performances of a refrigeration unit relying on compressors working in parallel have been investigated considering the influence of the compressor volumetric efficiency and isentropic efficiency on the compression ratio.Moreover,the following influential factors have been taken into account:evaporation temperature,condensation temperature and compressor suction-exhaust pressure ratio for different opening conditions of the compressor.The following quantities have been selected as the unit performance measurement indicators:refrigeration capacity,energy efficiency ratio(COP),compressor power consumption,and refrigerant flow rate.The experimental results indicate that the system refrigeration capacity and COP decrease with a decrease in evaporation temperature,increase of condensation temperature,and increase in pressure ratio.The refrigerant flow rate increases with the increase in evaporation temperature,decrease in condensing temperature and increase in pressure ratio.The compressor power consumption increases with the increase in condensing temperature and increase in pressure ratio,but is not significantly affected by the evaporation temperature.
文摘The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.
