Building Simulation最新文献

The increasing demand for cooling energy in data centers has become a global concern. Existing studies lack a comprehensive analysis of the energy performance of widely used multi-chiller cooling systems in air-cooled data centers throughout their lifecycle, especially concerning progressive loading. To bridge this gap, this study conducts a thorough assessment of the energy performance of multi-chiller cooling systems throughout the entire lifecycle. Additionally, the impact of climate conditions on the energy efficiency of the cooling systems is analyzed, considering design variations for typical climates. Multi-chiller cooling system models are developed using the test data of cooling equipment and typical control algorithms. The energy performance of the cooling system is thoroughly analyzed under full-range cooling loads and climate conditions. Results show that free cooling time could differ up to 1442 hours at different part load ratios in the same location. Furthermore, the cooling system’s coefficient of performance (COP) varies significantly, by up to 6, at different part load ratios, corresponding to a difference in power usage effectiveness (PUE) up to 0.14. Notably, the average cooling system COP throughout the lifecycle loading is found to be only 11.7, 2.9 lower than the design system COP.

In dental clinics with an open floor plan, the risk of patient-to-patient transmission of respiratory disease is a concern. During dental procedures large amounts of bioaerosol are produced and patients cannot wear personal protective equipment. This paper examines how to effectively deploy air cleaner to reduce the infection risk in dental clinics with an open floor plan. Various locations of air cleaners at various clean air delivery rates (CADRs) were investigated. The dispersion of bioaerosol was studied through numerical simulations, and risk assessment was performed by a dose-response method. The findings indicated that dental patients downstream of the background ventilation have a higher infection risk than those to the left and right of an infected patient (i.e., the source). The lowest infection risks for the adjacent patients were found when the air cleaner was place opposite to the dentists, i.e., on the floor at low CADR levels of 2.2 m³/min or on the bench at CADR levels of 4.4 m³/min or greater. The results of this study indicated that air cleaner can mitigate the risk of patient-to-patient transmission of SARS-CoV-2 in dental clinics with an open floor plan. Background CADR levels determine the optimal placement of air cleaners.

PM_2.5 pollution variations in different microenvironments would result in PM_2.5 exposure inequity between rural and urban residents. In this study, the real-time PM_2.5 exposure of urban and rural residents in China was examined based on portable PM_2.5 sensors together with activity patterns derived from questionnaire surveys, with a focus on students and senior citizens who are sensitive to air pollution. The results showed that PM_2.5 exposure varied significantly among different resident groups, with higher PM_2.5 exposure of rural residents than those of urban residents. PM_2.5 exposure peaks mostly occurred during (Accompanied) cooking activities owing to strong emissions. Sleeping and resting were the main activities that affected PM_2.5 exposures of different resident groups, accounting for 60.7%–94.5% of total daily exposures. Furthermore, the long duration of sleeping makes it the predominant activity contributing to PM_2.5 exposure inequity. It is necessary to obtain point-to-point respiratory volume (respiratory rate) data when measuring real-time PM_2.5 exposure data and incorporate respiratory volume (respiratory rate) into the analysis of PM_2.5 exposure. For the first time, this study quantified the PM_2.5 exposure inequality based on a novel method and can provide useful information for further studies on the exposure inequity.

Numerical heat, air and moisture (HAM) modeling allows predicting hygrothermal responses of building components with higher efficiency and less effort than laboratory experiments and field measurements. However, inaccuracy and/or incorrectness may appear in the predictions for the same case through different HAM models, primarily due to limitations or deviations in the description of physical phenomena and/or the implementation of mathematical algorithms. User preferences, biases, and/or mistakes with respect to implementing material properties, boundary conditions and other factors may also yield disparity. While a correct implementation of the numerical models is typically verified by the developers, the validity of the HAM models may remain questionable without the confrontation with experimental datasets. However, well-determined criteria and well-documented datasets for establishing the correct prediction of the transient hygrothermal responses of building components by HAM models remain very scarce. To address this issue, a dedicated benchmark experiment was conducted in the hot box-cold box (HB-CB) setup at KU Leuven, Belgium, on four wall assemblies composed of calcium silicate board, mineral wool, wood fiber board, and vapour barrier in different orders. Temperature, relative humidity, heat fluxes and moisture masses, as hygrothermal responses, were monitored under quasi-steady state boundary conditions. Full-scale characterization of the materials from the same batch was performed, along with a determination of the surface transport coefficients within the HB-CB setup. This comprehensive dataset allows a proper model validation by incorporating experimental datasets of material properties and surface transport coefficients and by confronting simulated hygrothermal responses with experimental evidence. In addition, sensitivity analysis can be performed to obtain insights into the impact of uncertainties in characterizing material properties on hygrothermal simulation predictions.

About 1/3 of human life is spent sleeping. The hypoxic and cold environment in high-altitude areas leads to sleep disorders that are more prominently harmful to the human body. To improve the quality of human sleep in high-altitude areas, this study explored the thermal and oxygen environment regulation for plateau sleep. In this study, the influencing factors of the diffusion of oxygenic–thermal coupled airflow were determined through the theoretical analysis of a thermal fluid mechanic jet. This study used computational fluid dynamics (CFD) to investigate the diffusion characteristics of the oxygenic–thermal coupled airflow with a sleeping experiment conducted on the plateau. The results showed that the influence of the thermal plume at 0.1 m near the human face was larger, and the oxygenic–thermal coupled airflow diffusion process was mainly divided into three phases over time. The size and time to stabilize the oxygen volume fraction in the inhalation zone varied between conditions and were strongly influenced by the temperature difference of the supply air. The effects of the thermal and oxygen environment were analyzed using indicators such as facial-area speed ratio, draft risk, and personal oxygen inhalation efficiency. The optimal design strategies were recommended with an outlet air velocity of 1.5 m/s, a temperature difference of 8 K between the outlet airflow and the indoor background air, and an outlet oxygen volume fraction of 30%. The results can provide implications for regulating the thermal and oxygen environment to improve human sleep quality in high-altitude areas.

Deep learning (DL), especially convolutional neural networks (CNNs), has been widely applied in air handling unit (AHU) fault diagnosis (FD). However, its application faces two major challenges. Firstly, the accessibility of operational state variables for AHU systems is limited in practical, and the effectiveness and applicability of existing DL methods for diagnosis require further validation. Secondly, the interpretability performance of DL models under various information scenarios needs further exploration. To address these challenges, this study utilized publicly available ASHRAE RP-1312 AHU fault data and employed CNNs to construct three FD models under three various information scenarios. Furthermore, the layer-wise relevance propagation (LRP) method was used to interpret and explain the effects of these three various information scenarios on the CNN models. An R-threshold was proposed to systematically differentiate diagnostic criteria, which further elucidates the intrinsic reasons behind correct and incorrect decisions made by the models. The results showed that the CNN-based diagnostic models demonstrated good applicability under the three various information scenarios, with an average diagnostic accuracy of 98.55%. The LRP method provided good interpretation and explanation for understanding the decision mechanism of CNN models for the unlimited information scenarios. For the very limited information scenario, since the variables are restricted, although LRP can reveal key variables in the model’s decision-making process, these key variables have certain limitations in terms of data and physical explanations for further improving the model’s interpretation. Finally, an in-depth analysis of model parameters—such as the number of convolutional layers, learning rate, β parameters, and training set size—was conducted to examine their impact on the interpretative results. This study contributes to clarifying the effects of various information scenarios on the diagnostic performance and interpretability of LRP-based CNN models for AHU FD, which helps provide improved reliability of DL models in practical applications.

Respiratory protection is critical to minimize airborne infection risk for healthcare workers. The main factor affecting infection risk in the medical scenario is leakage between the respirator and the healthcare workers’ face. However, it is difficult to quantify the effectiveness of respirators due to the numerous influencing factors. The medical activities were simulated by the fitting test, and the inverse of the results were used to get the total inward leakage of respirators. 114 subjects were divided according to gender, profession, age, risk area, and BMIs, participated in fitting test of four N95 respirators. The result showed that the value range of total inward leakage for tested N95 respirators was between 0.50% and 2.39%. Flat-folded respirators were more prone to leakage than cup-shaped ones, so it was essential to account for the impact of respirator shape. Similarly, this study found that gender and profession had no significant correlation with total inward leakage. Some medical procedures include bending, and turning head from side to side, or up and down, which had a significant influence on the protective efficacy of the N95 respirator. Facial dimensions and BMI exerted a more pronounced influence on the protective efficacy of the N95 respirator, and they were in the correlations. Healthcare personnels wore a well-fitting N95 respirator properly for excellent protection, reducing occurrence of nosocomial infections and occupational exposures, and optimizing respiratory protection strategies.

Non-intrusive load monitoring (NILM) technology aims to infer the operation information of electrical appliances from the total household load signals, which is of great significance for energy conservation and planning. However, existing methods are difficult to effectively capture the complex nonlinear features of the power consumption flow, which affects the energy disaggregation accuracy. To this end, this paper designs a method based on temporal convolutional network (TCN), efficient channel attention (ECA), and long short-term memory (LSTM). The method first creatively proposes a two-stage improved TCN (TSTCN), which overcomes its problems of extracting discontinuous information and poor correlation of long-distance information while enhancing the ability to extract high-level load features. Then a novel improved ECA attention mechanism (IECA) is embedded, which is also combined with the skip connection technique to pay channel-weighted attention to important feature maps and promote information fusion. Finally, the LSTM with strong temporal memory capability is introduced to learn the dependencies in the load power sequence and realize load disaggregation. Experiments on two real-world datasets, REDD and UK-DALE, show that the proposed model significantly outperforms other comparative NILM algorithms and achieves satisfactory tracking with the actual appliance operating power. The results show that the mean absolute error (MAE) of all appliances decreases by 18.67% on average, and the F1 score improves by 38.70%.

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.

Physical exercise spaces emerged as popular facilities due to recognizing the significance of physical well-being. This study investigates the relationship among physiological responses, human body energy transfer modes, and indoor environmental conditions in influencing thermal comfort perception within indoor physical exercise space. Seven male participants engaged in a 30 min constant-work-rate cycling exercise and a 20 min resting period in a climatic chamber. The physiological and environmental responses were recorded during the experiments, and the body’s energy transfer modes were calculated using the collected data. The dataset was prepared using the 2 min averages of the collected data and calculated parameters across the experiment phases, including the features of skin temperature, core temperature, skin relative humidity, heart rate, oxygen consumption, body’s heat transfer rates through convection, radiation, evaporation, and respiration, net metabolic heat production rate (metabolic rate minus external work rate), indoor air temperature, indoor relative humidity, air velocity, and radiant temperature. Gradient boosting regressor (GBR) was selected as the analyzing method to estimate predicted mean vote (PMV) and thermal sensation vote (TSV) indices during exercise and resting periods using features determined in the study. Thus, the four GBR models were defined as PMV-Exercise, PMV-Resting, TSV-Exercise, and TSV-Resting. In order to optimize the models’ performances, the hyperparameter tuning process was executed using the GridSearchCV method. A permutation feature importance analysis was performed, emphasizing the significance of net metabolic heat production rate (24.2%), radiant temperature (17.0%), and evaporative heat transfer rate (13.1%). According to the results, PMV-Exercise, PMV-Resting, and TSV-Resting GBR models performed better, while TSV-Exercise faced challenges in predicting exercise thermal sensations. Critically, this study addresses the need to understanding the interrelationship among physiological responses, environmental conditions, and human body energy transfer modes during both exercise and resting periods to optimize thermal comfort within indoor exercise spaces. The results of this study contribute to the operation of indoor gym environments to refine their indoor environmental parameters to optimize users’ thermal comfort and well-being. The study is limited to a small sample size consisting solely of male participants, which may restrict the generalizability of the findings. Future research could explore personalized thermal comfort control systems and synergies between comfort optimization and energy efficiency in indoor exercise spaces.