Ruixin Li

In the process of global urbanization, the shortage of land resources and traffic congestion are prominent. China’s urban rail transit has developed rapidly in recent years. At present, the public transport-oriented Transit-Oriented Development model with “transportation + business + residence” as the core is the core of the sustainable development of high urban rate. The underground space of Transit-Oriented Development faces extreme operational pressure and environmental comfort challenges in special periods such as the Spring Festival (personnel activities during weekends and important holidays in China) due to its strong closure, large population flow, high functional density, and the superposition of large passenger flow, commercial operation and rail transit activities. Due to the adult flow and complex physical field, the traditional single physical field research method has been unable to solve the problem of human comfort evaluation in complex environment. Based on the concept of sustainable development of underground space, this study takes a Transit-Oriented Development underground space in Zhengzhou City, central China as the research object. It explores the change law of multi-physical field environment of underground space under the superposition of ‘population density doubling and underground space shop operation’. The comprehensive comfort evaluation model suitable for this scene is established by Analytic Hierarchy Process–entropy weight method. It provides a theoretical basis for the design of Transit-Oriented Development underground space and the reduction in operating energy consumption.

With the increasing intelligence of modern air-conditioning systems, the difficulty of acquiring data from air-conditioning systems has been significantly reduced. However, analyzing the massive amounts of data collected and obtaining more valuable information still remains challenging, especially considering the internal relationships behind the data. The purpose of this study was to conduct operational experiments on VRF systems under different indoor set temperatures, indoor set air speeds, and terminal load rates. Then, the patterns of various operating parameters and energy consumption of VRF systems during winter operation were analyzed based on unsupervised methods. Three machine learning methods were primarily employed in this study, including correlation analysis, data regression analysis, and association rule analysis. Finally, a regression model was constructed for energy consumption based on eight typical characteristic parameters. The experimental results showed that the system was stable to a certain degree at different wind speeds. Among the characteristic parameters, fixed frequency 1 exhaust temperature, compressor frequency, and other parameters have a significant positive effect on energy consumption, while fixed frequency 1 shell top oil temperature, inlet and outlet pipe temperature difference, and other parameters have a negative effect. The research results provide a reference for air conditioning system data mining and building sustainability.

Clothing can absorb fine particulate pollutants from the atmosphere outdoors and then release them after entering the room in a form similar to three-handed smoke. In this study, the experimental site in Zhengzhou, a typical city with heavy pollution in central China, was used to investigate the adsorption and diffusion patterns of three different fabrics of polyester, polyester-cotton, and cotton clothing for fine particle pollutants, to guide peoples behavior patterns and indoor air distribution, and to reduce indoor pollution exposure. The results showed that when garments of three different fabrics were exposed to moderate and heavy PM_2.5 pollution for 1h, 2h, and 3h, respectively, and then transferred to the diffusion chamber, polyester always released the highest PM_2.5 concentration to indoor air during the same release cycle, followed by cotton and polyester-cotton. In addition, the concentration of indoor air pollutants will be periodically affected by the diffusion of fine particulate adsorbed by clothing fabrics. With the increase in outdoor pollution and exposure duration, the indoor PM_2.5 concentration takes longer to stabilize at certain levels after the clothing is transferred to the indoor side. Finally, the comparison of natural ventilation experiments proved that it is not feasible to rely solely on natural ventilation to improve the influence of clothing pollution sources on indoor air quality.

Since the 21st century, in several public health emergencies that have occurred across the world, the humid enclosed environment of the toilet has become one of the places where bacteria, viruses, and microorganisms breed and spread. Mobile public toilets, as a supplement of urban fixed public toilets, are also widely used in densely populated areas. According to statistics, since the outbreak of COVID-19 in 2019, multiple incidents of people being infected by the COVID-19 virus due to aerosol proliferation in public toilets have been confirmed. It is an urgent issue to resolve the internal environmental pollution of mobile public health and reduce the risk of virus transmission in public spaces under the global epidemic prevention. This paper utilized a typical combined mobile public toilet as the research object and measured and evaluated the indoor thermal environment in real time over a short period of time. The diffusion mode and concentration change law of pollutants in mobile public toilets were predicted and analyzed based on CFD. Regression analysis was also used to clarify the relationship between indoor thermal environment variables and aerosol diffusion paths, and a ventilation optimization scheme was proposed to reduce the risk of virus transmission.

The intermittent heating mode of Kang plays an important role in the heat storage and release in cave dwellings. However, research on the effect of Kang heating on the thermal process of traditional buildings is rare. Therefore, based on long-term monitoring of cave dwellings, regular conclusions about the influence of Kang heating on the thermal environment were obtained. Furthermore, an unsteady heat transfer model of the envelope was proposed for the first time. Then, based on this model, the thermal storage performance of cave dwellings during the period of Kang intermittent heating was explored. The results showed that, due to Kang heating, the indoor air temperature of cave dwellings could be increased by an average of 3.1 °C. Furthermore, the inner walls had a large thermal mass and the maximum heat storage in a single day was 487.75 kJ/m², while the maximum heat release was 419.02 kJ/m². The heat release at night could reach 87%. In this paper, the law of thermal storage and release characteristics of earthen building envelopes under intermittent heating was firstly obtained. Results can enrich the thermal process theory of earthen buildings and provide a theoretical basis and technical support for building thermal environmental construction.

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.

Solar air collectors installed on buildings can significantly reduce conventional energy consumption in winter and summer. However, some problems arise in the utilization process, such as overheating, inconvenient operation control and low energy efficiency, etc. This work is a parametric analysis focusing on the automatic control and thermal efficiency improvement of the solar wall. An improved color-changing solar wall integrated with automatic control components, such as a photoelectric fan and temperature-controlled damper, was proposed in this paper. Based on the experimental data, the average daily heat output of the color-changing solar wall is 1.08 MJ per unit floor area on clear days in winter and the average thermal efficiency is 56.8%. Meanwhile, a quantitative analysis was carried out based on monitoring experiments for evaluating the thermal characteristic of automatic control components. Furthermore, in order to improve the thermal performance of the solar wall, parametric analysis was performed by numerical simulation. Results from this paper can provide a theoretical basis for the application of solar air collectors in modern buildings.

Near-wall microenvironment of a building refers to parameters such as wind speed, temperature, relative humidity, solar radiation near the building’s façade, etc. The distribution of these parameters on the building façade shows a certain variation based on changes in height. As a technology of passive heating and ventilation, the effectiveness of this application on heat collection wall is significantly affected by the near-wall microclimate, which is manifested by the differences, and rules of the thermal process of the components present at different elevations. To explore the feasibility and specificity of this application of heat collection wall in high-rise buildings, this study uses three typical high-rise buildings from Zhengzhou, China, as research buildings. Periodic measurements of the near-wall microclimate during winter and summer were carried out, and the changing rules of vertical and horizontal microclimate were discussed in detail. Later, by combining these measured data with numerical method, thermal process and performance of heat collection wall based on increasing altitude were quantitatively analyzed through numerical calculations, and the optimum scheme for heat collection wall components was summarized to provide a theoretical basis for the structural design of heat-collecting wall in high-rise buildings.