Building Simulation最新文献

Face masks’ wearing for a long duration brings thermal discomfort, especially in hot climate cities. The face masks’ thermal insulation and its effect on outdoor thermal comfort have been rarely investigated. In this study, five types of face masks and their thermal insulations have been tested by using a thermal manikin in the climate chamber. Experimental results are assessed by using physiological equivalent temperature (PET) and standard effective temperature (SET*) for thermal comfort with masks at three walking speeds both in summer and winter. Slight differences in thermal insulation are observed among the different masks, the values of PET and SET* rise with increasing mask thermal insulation, and they are generally higher in summer than in winter. Moreover, the variation of SET* is more obvious than PET with same masks at different walking speeds. And the differences of SET* with and without masks appear to rise significantly for fast walking. Results further indicate that the individuals’ physical discomfort caused by wearing masks cannot simply be assumed as an additional effect of the clothing thermal insulation. The findings enrich the clothing thermal insulation database, explore the differences in thermal indices if the face mask is used, and provide advice on heat mitigation with masks outdoors.

The concept of a soundmark refers to a distinctive sound source that identifies a particular area based on its environmental acoustic characteristics. This study investigated the factors and processes of soundmark identification in urban parks. After interviewing participants in 14 different urban parks in China, six categories that affect soundmark identification were identified, “sound source”, “context”, “sound perception”, “individual features”, “response” and “comprehension”. First, the core category is context. As for context, in amusement parks, activity parks, and nature parks, respectively, the theme, function, and scene affect the soundmark identification. For sound perception, soundmark identification is affected by the occurrence of birdsongs and the duration of water sounds in nature parks. Soundmark identification is affected by contrast, duration, and audio-visuals in amusement parks and activity parks. Furthermore, a soundmark identification model specifically for urban parks has been established. The model contains two identified processes: responsive soundmarks and comprehensive soundmarks. For responsive soundmark identification processes, sound source, context, sound perception, and response are essential factors. For comprehensive soundmark identification processes, sound source, context, individual features, and comprehension are essential factors. The soundmarks cannot be identified if the response or comprehension is missed during the process. Ultimately, the park’s cultural features promote the identification of comprehensive soundmarks. In addition, soundmarks can be effectively transformed by changing the context of urban parks or the characteristics of sound perception.

This research investigates retrofitting strategies for multifunctional spaces within educational buildings, employing agent-based and performance-based modeling to support decision-making. An experimental matrix was developed, reflecting three usage scenarios (reading, exhibition, lecture) across four retrofitting schemes. An agent-based model was developed to delineate intricate human behaviors in space and examined the self-organizing behaviors of 30 agents for each scheme in every scenario, evaluating six metrics on spatial efficiency and visual experience. Calibrated models, derived from real data and processed through DesignBuilder software, evaluated three metrics: energy use, thermal comfort, and visual comfort. The research then incorporated metrics from the agent-based model and performance simulation to develop a method for discussing the decision-making process in retrofit strategies. The findings indicate that the optimal retrofitting solution for multifunctional spaces is heavily influenced by the distribution of usage scenarios. Given the substantial influence of space metrics on selecting the optimal retrofit scheme, the proposed framework effectively facilitates decision-making for building retrofits by providing a holistic evaluation of both spatial and energy criteria.

This study focused on the effect of glass structures of modern architecture on the indoor thermal environment during summer. In particular, this study examined how solar radiation significantly altered people’s thermal sensations. Laboratory tests on convection–radiation air conditioning systems were conducted, encompassing 12 different scenarios, including diverse indoor open areas, terminal forms, and levels of solar radiation. These tests aimed to explore the physiological and psychological responses of the human body to solar radiation penetrating through windows into the inner room. During the experiments, the participants’ subjective thermal sensations and thermal comfort were recorded, along with continuous monitoring of their physiological and environmental parameters. Results showed that solar radiation significantly increased local skin temperature, with a maximum rise of 2.15 °C. Operative temperature is a reliable indicator of human skin temperature and thermal sensation vote (TSV). This study established two models that could predict the skin temperature of individuals indoors through operative temperature under conditions without or with solar radiation, and identified sensitive ranges of operative temperature for both models, to be specific, 26.32 °C to 28.43 °C and 28.51 °C to 34.11 °C, respectively. Furthermore, this study established the relationship between skin temperature and TSV under conditions with and without solar radiation. The results indicate that solar radiation enhances the human body’s adaptability to indoor environmental parameters; a convection–radiation system (FC+RF) could be used to optimize indoor thermal control under solar radiation, achieving more stable environmental temperatures and improved indoor comfort.

The “average occupant” methodology is widely used in energy consumption simulations of residential buildings; however, it fails to consider the differences in energy use behavior among family members. Based on a field survey on the Central Shaanxi Plain, to identify the energy use behavior patterns of typical families, a stochastic energy use behavior model considering differences in energy use behavior among family members was proposed, to improve the accuracy of energy consumption simulations of residential buildings. The results indicated that the surveyed rural families could be classified into the following four types depending on specific energy use behavior patterns: families of one elderly couple, families of one middle-aged couple, families of one elderly couple and one child, and families of one couple and one child. Moreover, on typical summer days, the results of daily building energy consumption simulation obtained by the “average occupant” methodology were 25.39% and 28% lower than the simulation results obtained by the model proposed in this study for families of one elderly couple and families of one middle-aged couple, and 13.05% and 23.05% higher for families of one elderly couple and one child, and families of one couple and one child. On typical winter days, for the four types of families, the results of daily building energy consumption simulation obtained by the “average occupant” methodology were 21.69%, 10.84%, 1.21%, and 8.39% lower than the simulation results obtained by the model proposed in this study, respectively.

Fast flow simulation is imperative in the design of pulsating ventilation, which is potentially efficient in indoor air contaminant removal. The execution of the conventional CFD method requires considerable amount of computational resources. In this study, five different numerical schemes were proposed based on fast fluid dynamics (FFD) and fractional step (FS) methods, and were evaluated to achieve quick simulation of airflow/contaminant dispersion. One of these numerical schemes was identified with the best overall computing efficiency for investigating the performance of pulsating ventilation. With this numerical scheme at hand, the air contaminant removal effectiveness of different ventilation types was evaluated. Two kinds of pulsating ventilation and one kind of steady ventilation were tested upon a benchmark isothermal mixing chamber. The effect of adjusting supply velocity parameters on the ventilation performance was also investigated. CO₂ concentration, airflow pattern, and vortex structure of different ventilation types were illustrated and analyzed. The results reveal that the FS method is more suitable for transient simulation of wall-bounded indoor airflow than the FFD method, and 34%–51% of computing time could be saved compared to the conventional CFD method. Regarding the choice of ventilation type, steady ventilation might result in short-circuit airflow and stagnant zones; alternatively, pulsating ventilation has greater potential in air contaminant removal due to its ever-changing vortex structure.

There is a growing need in public health to conduct large-scale epidemiological studies to investigate the health effects of fine particulate matter (PM_2.5) exposure levels. In response to this need, we developed a real-time personal PM_2.5 exposure monitoring system (PEPS: Personal Exposure PM_2.5 System), which is capable of monitoring personal exposure concentration and uploading data in real time. The air quality self-labelling device, specifically customized for the PEPS, can be worn on the body and features functions for real-time data automatic upload, data storage, data export, and localization. This system enables researchers to obtain the big data of personal PM_2.5 exposure concentration at low cost, with minimal manpower and technical requirements. It has been utilized to investigate the personal exposure levels of PM_2.5 among college students in Beijing, China, providing a substantial volume of valuable data for indoor air quality and related epidemiological study. The maximum difference between the monitored daily average exposure concentration and the outdoor concentration was 265 µg/m³, corresponding to a relative error of 1579.5%. The correlation analysis of 11 factors showed that the correlation between exposure concentration and outdoor concentration was as high as 0.66 (p < 0.001), and the correlation between exposure concentration and other certain factors was in the range of [−0.11, −0.03].

Thermally activated building envelopes (TABEs) are multifunctional component that combines structural and energy properties. Based on re-examining the heat charging processes, an arc-shaped metal-fin-enhanced TABE (Arc-finTABE) with directional heat charging features is proposed to optimize the thermal barrier formation process. A comprehensive parameterized analysis is conducted based on a validated mathematical model to explore the influence of 5 fin-structure design parameters and the static insulation thickness. Results verified that the directional charging strengthening fins can improve transient thermal performances of Arc-finATBE and enlarge horizontal and vertical sizes of the thermal energy accumulation area surrounding the pipeline, while the maximum growth in extra heat loss is less than 3.17%. From the perspective of promoting heat injection into expected areas, the straight main fin configurations with the angle of main fins of 30°, shank length ratio of 0.4 and no leftward mounted fins are preferred in load-reduction mode, while the angle of main fins of 150°, shank length ratio of 0.8 and multiple fin designs, especially with one of the main fins horizontally toward the indoor side, are more favorable in auxiliary-heating mode. Besides, it is recommended to add one arc-shaped branch fin to each main fin to achieve a balance between performance improvement and material usage. Moreover, branch fins with larger arc angles are preferred in auxiliary-heating mode, while smaller arc angles are conducive to injecting heat into the wall along main fins in load-reduction mode and preventing the heat near the inner surface from being extracted. Under the direct influence of the strengthened invisible thermal barrier, Arc-finTABEs can reduce the amount of static insulation layer by 20%–80% while achieving equivalent thermal performances as conventional high-performance walls.

The prediction of building energy consumption offers essential technical support for intelligent operation and maintenance of buildings, promoting energy conservation and low-carbon control. This paper focused on the energy consumption of heating, ventilation and air conditioning (HVAC) systems operating under various modes across different seasons. We constructed multi-attribute and high-dimensional clustering vectors that encompass indoor and outdoor environmental parameters, along with historical energy consumption data. To enhance the K-means algorithm, we employed statistical feature extraction and dimensional normalization (SFEDN) to facilitate data clustering and deconstruction. This method, combined with the gated recurrent unit (GRU) prediction model employing adaptive training based on the Particle Swarm Optimization algorithm, was evaluated for robustness and stability through k-fold cross-validation. Within the clustering-based modeling framework, optimal submodels were configured based on the statistical features of historical 24-hour data to achieve dynamic prediction using multiple models. The dynamic prediction models with SFEDN cluster showed a 11.9% reduction in root mean square error (RMSE) compared to static prediction, achieving a coefficient of determination (R²) of 0.890 and a mean absolute percentage error (MAPE) reduction of 19.9%. When compared to dynamic prediction based on single-attribute of HVAC systems energy consumption clustering modeling, RMSE decreased by 12.6%, R² increased by 4.0%, and MAPE decreased by 26.3%. The dynamic prediction performance demonstrated that the SFEDN clustering method surpasses conventional clustering method, and multi-attribute clustering modeling outperforms single-attribute modeling.

The air conditioning (A/C) of cabins allows for customized control, but manual adjustments may distract drivers, as well as result in energy inefficiency. Several existing thermal sensation models require complex inputs, which are challenging to gather whilst driving. To address this issue, this study developed a non-contact thermal sensation model for cabin occupants based on thermal imaging sensor. To collect actual data used for modeling, an outdoor subject experiment was conducted. In this study, initial training was conducted to compare the performance of six algorithms in building the model, with random forests algorithm showing the best performance. Besides, this study employed the recursive feature elimination (RFE) method with cross-validation algorithm for identifying the key features. In the end, the model was retrained using the selected features. The model that incorporated both environmental parameters and facial-temperature features demonstrated the best performance, with an R² of 0.659 on the test set. Eliminating the hard-to-measure windshield surface temperature resulted in a slight reduction in accuracy, yielding an R² of 0.651. To verify the generalizability of the model, this study further conducted independent validation experiments. The selected model, which exhibited a mean absolute error (MAE) of less than 0.4 in thermal sensation units, was proven to be highly applicable. The results can offer new solutions for automatic control of cabin A/C.