The characteristics of the transcritical CO₂ heat pump water heater (HPWH) system are; a lower inlet hot water temperature (T_i-hw) (sometimes this is lower than the water source temperature), and an outlet gas cooler temperature (T_o-gc) which is affected by the T_i-hw and often lower than the critical temperature. In order to study the effects of the internal heat exchanger (IHX) on the operational performance of the transcritical CO₂ HPWH when T_o-gc is low, a transcritical CO₂ water source HPWH experiment platform is established to conduct experimental research and comparative analysis on the operational performance of the transcritical CO₂ water source HPWH, with or without IHX. It is found that, if only the coefficient of performance (COP) and heating at the optimal exhaust pressure of the transcritical CO₂ water source HPWH were considered, COP and the heating of the non-IHX system would be slightly higher than those of the IHX system at the lower hot water flow and water source temperature, and this increase was not obvious. At the higher hot water flow rate and water source temperature, COP and the heating of the non-IHX system were also higher than those of the IHX system, and the increase was obvious. The experiment results showed that, near the optimal exhaust pressure, the variation range of COP and heating of the IHX system is relatively small, and the system has a relatively high stability.

The flow in the gap between the reed and the valve seat has a significant influence on the dynamic characteristics of the reed valve used in reciprocating compressors. The fluid–structure interaction (FSI) method is an effective method for studying reciprocating compressors. A three-dimensional FSI model of a reciprocating compressor with a reed valve is established in this paper, which has an important influence on the flow rate characteristic of reciprocating compressors. Furthermore, an experimental investigation is implemented to verify the FSI model. Based on the established FSI model, the pressure distribution on the reed valve surface is identified by varying the height of the suction valve limiter and the rotational speed of the compressor, which has an important effect on the dynamic characteristics of the reed valve. Although the low-pressure region, due to the Bernoulli effect on the surface of the reed, hinders the rapid opening of the valve to some extent, it is obviously beneficial to the timely closure of the valve and increases the volumetric efficiency of the compressor. Moreover, the optimal height of the valve limiter and the appropriate rotational speed of the compressor are obtained.

The effect of the discharge pressure on the performance of the trans-critical CO₂ heat pump with a low gas-cooler outlet temperature is experimentally investigated on a test rig of water source heat-pump water heater. The optimal discharge pressure of the trans-critical CO₂ heat pump is investigated under different external operation conditions. When the tap-water temperature is low, the characteristic of the S-shape isotherm at the supercritical region has little effect on the occurrence of the optimal discharge pressure; while the mass flow rate of CO₂, the suction pressure and the gas-cooler outlet temperature play a significant role in determining the emergence of the optimal discharge pressure. At the optimal discharge pressure, the COP reaches the peak; however, the corresponding heating capacity is still lower than its maximum, which is reached as the discharge pressure is slightly above the optimal discharge pressure. Reducing the tap-water flowrate or increasing the water-source temperature can increase the optimal discharge pressure. The COP is positively dependent on both the tap-water flowrate and the water-source temperature. In addition, the tap-water flowrate has a negligible influence on the maximum heating capacity while increasing the water-source temperature can greatly enhance the heating capacity.

This work provides a case study on the indoor environment and ventilation rate of naturally ventilated research student rooms in Chinese universities. In the measured room, air temperature, relative humidity and carbon dioxide (CO₂) concentration were monitored during the heating period for 4 weeks. The number of indoor occupants, occupied time of the room and window/door-opening cases were simultaneously recorded. Results showed the research student room was occupied for an average of 12.0 h each day. Due to a large indoor and outdoor temperature difference during the heating season, and occupants’ adaption to indoor environment, indoor occupants seldom open windows/doors for ventilation. Air exchange of the room only by air infiltration cannot meet the ventilation requirement. As a result, an average of 77.6% of measured CO₂ data each day exceeded 1000 ppm during occupied time. In fact, according to CO₂ data, it was observed that window/door opening could effectively decrease indoor CO₂ concentration. Therefore, intermittent window/door opening or CO₂-based demand-controlled ventilation facilities were suggested for improving indoor air quality of such rooms. Additionally, special attention should be paid to other possible outdoor pollution.

China’s “dual carbon” goals, energy conservation and emission reduction in the energy system, have become increasingly important. The sensor fault of an energy system will cause unstable operation and increase energy consumption. Therefore, this study proposes a new sensor fault detection strategy based on the data driven method for energy saving and emission reduction. However, for data-driven models, data quality has a greater impact on model performance. This study innovatively uses five machine learning methods to optimize the energy system operating data. Five machine learning methods include the moving average (MA), Lowess, Loess, Rlowess and Rloess methods. Fault detection performances of different data driven models optimized by different approaches are compared and analyzed. Besides, data outliers and parameter selection of data optimization methods are discussed. The results indicate that the MA method has the best optimization performance when the smoothness degree is level 2. The optimized data fluctuation range is controlled within the range of ±1. The fault detection accuracy rate of the model optimized based on the MA method is increased from 32.51% to 83.96% when the evaporation temperature sensor fault is a 5 °C deviation. However, the data will deviate from the original data trend when the smoothing parameter is set too large. Therefore, the smoothness of the data should not be too large. The approach proposed in this study is of great significance to the energy saving and emission reduction of the energy system.