To address the issue of unreasonable buried pipe length design in ground-source heat pump projects, a three-dimensional dynamic simulation platform was developed. The reasonableness of the buried pipe length was evaluated by comparing the simulated outlet temperature with the designed outlet temperature based on specifications. Using a building in Beijing as a case study, the effects of soil thermal properties and borehole-related parameters on the design error in buried pipe length were analyzed. A sensitivity analysis further examined the impact of these factors. Results indicate that the relative error in buried pipe design length increases with rising soil thermal conductivity, soil volumetric heat capacity, borehole depth, and borehole spacing. Relative error ranges were 10.7%-27.3%, 8.0%-23.8%, 7.3%-12.5%, and 12.5%-17.4% for the respective factors. Sensitivity analysis revealed soil thermal conductivity as the most significant factor influencing pipe length, with a quantitative index of 0.909. Other influential factors, in descending order, were soil volumetric heat capacity, borehole spacing, number of borehole columns, and borehole depth.

Applying data-driven fault-diagnosis models to data center air-conditioning systems can significantly improve operational reliability. However, these models often lack diagnostic interpretability, which limits their application. This study develops a composite fault-diagnosis model based on typical machine-learning algorithms, compares the diagnostic performance of different models, and conducts interpretability research on the diagnostic models using the Shapley additive explanation method. The results demonstrate that the convolutional neural network (CNN)-based fault-diagnosis model achieves optimal performance in both the heat-pipe and vapor-compression modes, with F-1 scores exceeding 0.999 across all classifications. In the heat-pipe mode, the diagnosis of the CNN model primarily relies on the condenser-fan frequency, outdoor temperature, and refrigerant-pump power consumption as key features, whereas in the vapor-compression mode, the dominant features are the outdoor temperature, compressor frequency, and subcooling degree.

Fundamental metrology is closely tied to scientific advancement and requires well-equipped facilities to achieve low measurement uncertainty in rigorous experiments. Addressing the ±0.1 K high-stability temperature control issue of the precision laboratory radiant air conditioning system, this study investigated the influence of different radiant panel area ratios, laying methods, cold source water supply temperature fluctuations, and external environmental disturbances by simulations. The results indicate that: (1) the larger the ratio of radiant panel area, the greater the fluctuation in equipment surface temperature; (2) the surface temperature of the measurement equipment can satisfy ±0.1 K control temperature stability requirement when the fluctuations of the surface temperature of radiant panels and glass window are within ±0.5 K and ±1 K respectively without radiant panels on the ceiling; (3) the surface temperature of the measurement equipment can satisfy ±0.1 K control temperature stability requirement when the fluctuations of the surface temperature of radiant panels and glass window are within ±0.2 K and ±2 K respectively with radiant panels on the ceiling. This study provides a reference for the design and operation control of air conditioning systems in fundamental metrology.