Building Simulation建筑模拟
Building Simulation(英文缩写 BUILD SIMUL-CHINA),ISSN 1996-3599,eISSN 1996-8744,中文译名:建筑模拟 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 4.008 | Q2 |
| 2022 | 5.500 | Q1 |
| 2023 | 6.100 | Q1 |
| 2024 | 5.900 | Q1 |
| 2025 | 6.900 | Q1 |
Building Simulation 最新收录文献
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1. Using urban building energy modeling to quantify the energy performance of residential buildings under climate change.
PMID:日期:2023-06-02The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization. Urban building energy modeling (UBEM) is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations, supporting the implementation of carbon emission reduction policies. Currently, most studies focus on the energy performance of archetype buildings under climate change, which is hard to obtain refined results for individual buildings when scaling up to an urban area. Therefore, this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas, by taking two urban neighborhoods comprising 483 buildings in Geneva, Switzerland as case studies. In this regard, GIS datasets and Swiss building norms were collected to develop an archetype library. The building heating energy consumption was calculated by the UBEM tool-AutoBPS, which was then calibrated against annual metered data. A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%. The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results showed a decrease of 22%-31% and 21%-29% for heating energy consumption, an increase of 113%-173% and 95%-144% for cooling energy consumption in the two neighborhoods by 2050. The average annual heating intensity dropped from 81 kWh/m in the current typical climate to 57 kWh/m in the SSP5-8.5, while the cooling intensity rose from 12 kWh/m to 32 kWh/m. The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7% and 18.6%, respectively, in the SSP scenarios. The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.
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2. Numerical investigation of impinging jet ventilation in ICUs: Is thermal stratification a problem?
PMID:日期:2023-05-11Intensive care units (ICUs) are the high incidence sites of hospital-acquired infections, where impinging jet ventilation (IJV) shows great potential. Thermal stratification of IJV and its effect on contaminants distribution were systematically investigated in this study. By changing the setting of heat source or the air change rates, the main driving force of supply airflow can be transformed between thermal buoyancy and inertial force, which can be quantitatively described by the dimensionless buoyant jet length scale (). For the investigated air change rates, namely 2 ACH to 12 ACH, varies between 0.20 and 2.80. The thermal buoyancy plays a dominant role in the movement of the horizontally exhaled airflow by the infector under low air change rate, where the temperature gradient is up to 2.45 °C/m. The flow center remains close to the breathing zone of the susceptible ahead, resulting into the highest exposure risk (6.6‰ for 10-µm particles). With higher heat flux of four PC monitors (from 0 W to 125.85 W for each monitor), the temperature gradient in ICU rises from 0.22 °C/m to 1.02 °C/m; however, the average normalized concentration of gaseous contaminants in the occupied zone is reduced from 0.81 to 0.37, because their thermal plumes are also able to carry containments around them to the ceiling-level readily. As the air change rate was increased to 8 ACH (), high momentum weakened the thermal stratification by reducing the temperature gradient to 0.37 °C/m and exhaled flow readily rose above the breathing zone; the intake fraction of susceptible patient located in front of the infector for 10-µm particles reduces to 0.8‰. This study proved the potential application of IJV in ICUs and provides theoretical guidance for its appropriate design.}.
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3. Impact of thermal stratification on airborne transmission risk of SARS-CoV-2 in various indoor environments.
PMID:日期:2023-05-09There exist various vertical temperature gradients in different-type buildings. A holistic understanding of the impact of different temperature-stratified indoor environments on infection risk is necessary. In this work, the airborne transmission risk of SARS-CoV-2 in different thermally stratified indoor environments is assessed using our previously developed airborne infection risk model. Results show that the vertical temperature gradients in office building, hospital, classroom, etc. are within the range of -0.34 to 3.26 °C/m. In large space such as coach station, airport terminal, and sport hall, the average temperature gradient ranges within 0.13-2.38 °C/m in occupied zone (0-3 m); in ice rink with special requirements of indoor environment, the temperature gradient is higher than those in the above indoor spaces. The existence of temperature gradients causes multi-peaks of the transmission risk of SARS-CoV-2 with distancing, and our results show that in office, hospital ward and classroom, the second peak of the transmission risk is higher than 10 in most contact scenarios, while most being lower than 10 in large spaces like coach station and airport. The work is expected to provide some guidance on specific intervention policies in relation to the types of indoor environments. the Appendix is available in the online version of this article at 10.1007/s12273-023-1021-5.
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4. Predicting building ventilation performance in the era of an indoor air crisis.
PMID:日期:2023-04-25该文献暂无摘要。
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5. Multi-objective optimization of mechanical ventilation with the aid of purifiers in two scenarios: Regular operation and mitigating the spread of respiratory infectious diseases.
PMID:日期:2023-04-25COVID-19 and its impact on society have raised concerns about scaling up mechanical ventilation (MV) systems and the energy consequences. This paper attempted to combine MV and portable air cleaners (PACs) to achieve acceptable indoor air quality (IAQ) and energy reduction in two scenarios: regular operation and mitigating the spread of respiratory infectious diseases (RIDs). We proposed a multi-objective optimization method that combined the NSGA-II and TOPSIS techniques to determine the total equivalent ventilation rate of the MV-PAC system in both scenarios. The concentrations of PM and CO were primary indicators for IAQ. The modified Wells-Riley equation was adopted to predict RID transmissions. An open office with an MV-PAC system was used to demonstrate the method's applicability. Meanwhile, a field study was conducted to validate the method and evaluate occupants' perceptions of the MV-PAC system. Results showed that optimal solutions of the combined system can be obtained based on various IAQ requirements, seasons, outdoor conditions, etc. For regular operation, PACs were generally prioritized to maintain IAQ while reducing energy consumption even when outdoor PM concentration was high. MV can remain constant or be reduced at low occupancies. In RID scenarios, it is possible to mitigate transmissions when the quanta were < 48 h. No significant difference was found in the subjective perception of the MV and PACs. Moreover, the effects of infiltration on the optimal solution can be substantial. Nonetheless, our results suggested that an MV-PAC system can replace the MV system for offices for daily use and RID mitigation. The Appendix is available in the online version of this article at 10.1007/s12273-023-0999-z.
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6. A cost-effectiveness assessment of the operational parameters of central HVAC systems during pandemics.
PMID:日期:2023-03-26The present study develops a cost-effectiveness assessment model to analyze the performance of major operational parameters of central HVAC systems in terms of airborne transmission risk, energy consumption, and medical and social cost. A typical multi-zone building model with a central HVAC system is built numerically, and the effect of outdoor air (OA) ratio (from 30% to 100%) and filtration level (MERV 13, MERV 16, and HEPA) are assessed under the conditions of five climate zones in China. Compared with the baseline case with 30% OA and MERV 13 filtration, the airborne transmission risk in zones without infector is negligibly reduced with the increase in OA ratio and the upgrade of filtration level, owing to their slight modification on the equivalent ventilation rate of virus-free air. However, depending on climate zone, a 10% increase in OA ratio results in 12.5%-78.6% and 0.1%-8.6% increase in heating and cooling energy consumption, respectively, while an upgrade of filtration level to MERV 16 and HEPA results in an increase of 0.08%-0.2% and 1.4%-2.6%, respectively. Overall, when compared to the use of 100% OA ratio and HEPA filtration, the application of 30% or 40% OA ratio and MERV 13 filtration would save annually an energy and facility related cost of $29.4 billion in China, though giving an increase of approximately $0.1 billion on medical and social cost from the increased number of confirmed cases. This study provides basic method and information for the formulation of cost-effective operational strategies of HVAC systems coping with the airborne transmission, especially in resource-limited regions.
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7. Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence.
PMID:日期:2023-03-13Prediction of indoor airflow distribution often relies on high-fidelity, computationally intensive computational fluid dynamics (CFD) simulations. Artificial intelligence (AI) models trained by CFD data can be used for fast and accurate prediction of indoor airflow, but current methods have limitations, such as only predicting limited outputs rather than the entire flow field. Furthermore, conventional AI models are not always designed to predict different outputs based on a continuous input range, and instead make predictions for one or a few discrete inputs. This work addresses these gaps using a conditional generative adversarial network (CGAN) model approach, which is inspired by current state-of-the-art AI for synthetic image generation. We create a new Boundary Condition CGAN (BC-CGAN) model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter, such as a boundary condition. Additionally, we design a novel feature-driven algorithm to strategically generate training data, with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the AI model. The BC-CGAN model is evaluated for two benchmark airflow cases: an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated box. We also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error criteria. The results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5% relative error and up to about 75,000 times faster when compared to reference CFD simulations. The proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the AI models while maintaining prediction accuracy, particularly when the flow changes non-linearly with respect to an input.
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8. Machine learning approach for estimating the human-related VOC emissions in a university classroom.
PMID:日期:2023-03-13Indoor air quality becomes increasingly important, partly because the COVID-19 pandemic increases the time people spend indoors. Research into the prediction of indoor volatile organic compounds (VOCs) is traditionally confined to building materials and furniture. Relatively little research focuses on estimation of human-related VOCs, which have been shown to contribute significantly to indoor air quality, especially in densely-occupied environments. This study applies a machine learning approach to accurately estimate the human-related VOC emissions in a university classroom. The time-resolved concentrations of two typical human-related (ozone-related) VOCs in the classroom over a five-day period were analyzed, i.e., 6-methyl-5-hepten-2-one (6-MHO), 4-oxopentanal (4-OPA). By comparing the results for 6-MHO concentration predicted via five machine learning approaches including the random forest regression (RFR), adaptive boosting (Adaboost), gradient boosting regression tree (GBRT), extreme gradient boosting (XGboost), and least squares support vector machine (LSSVM), we find that the LSSVM approach achieves the best performance, by using multi-feature parameters (number of occupants, ozone concentration, temperature, relative humidity) as the input. The LSSVM approach is then used to predict the 4-OPA concentration, with mean absolute percentage error (MAPE) less than 5%, indicating high accuracy. By combining the LSSVM with a kernel density estimation (KDE) method, we further establish an interval prediction model, which can provide uncertainty information and viable option for decision-makers. The machine learning approach in this study can easily incorporate the impact of various factors on VOC emission behaviors, making it especially suitable for concentration prediction and exposure assessment in realistic indoor settings.
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9. Tracing the origin of large respiratory droplets by their deposition characteristics inside the respiratory tract during speech.
PMID:日期:2023-03-13Origin of differently sized respiratory droplets is fundamental for clarifying their viral loads and the sequential transmission mechanism of SARS-CoV-2 in indoor environments. Transient talking activities characterized by low (0.2 L/s), medium (0.9 L/s), and high (1.6 L/s) airflow rates of monosyllabic and successive syllabic vocalizations were investigated by computational fluid dynamics (CFD) simulations based on a real human airway model. SST model was chosen to predict the airflow field, and the discrete phase model (DPM) was used to calculate the trajectories of droplets within the respiratory tract. The results showed that flow field in the respiratory tract during speech is characterized by a significant laryngeal jet, and bronchi, larynx, and pharynx-larynx junction were main deposition sites for droplets released from the lower respiratory tract or around the vocal cords, and among which, over 90% of droplets over 5 µm released from vocal cords deposited at the larynx and pharynx-larynx junction. Generally, droplets' deposition fraction increased with their size, and the maximum size of droplets that were able to escape into external environment decreased with the airflow rate. This threshold size for droplets released from the vocal folds was 10-20 µm, while that for droplets released from the bronchi was 5-20 µm under various airflow rates. Besides, successive syllables pronounced at low airflow rates promoted the escape of small droplets, but do not significantly affect the droplet threshold diameter. This study indicates that droplets larger than 20 µm may entirely originate from the oral cavity, where viral loads are lower; it provides a reference for evaluating the relative importance of large-droplet spray and airborne transmission route of COVID-19 and other respiratory infections.
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10. Quantifying spatiotemporal variability in occupant exposure to an indoor airborne contaminant with an uncertain source location.
PMID:日期:2023-03-05Well-mixed zone models are often employed to compute indoor air quality and occupant exposures. While effective, a potential downside to assuming instantaneous, perfect mixing is underpredicting exposures to high intermittent concentrations within a room. When such cases are of concern, more spatially resolved models, like computational-fluid dynamics methods, are used for some or all of the zones. But, these models have higher computational costs and require more input information. A preferred compromise would be to continue with a multi-zone modeling approach for all rooms, but with a better assessment of the spatial variability within a room. To do so, we present a quantitative method for estimating a room's spatiotemporal variability, based on influential room parameters. Our proposed method disaggregates variability into the variability in a room's average concentration, and the spatial variability within the room relative to that average. This enables a detailed assessment of how variability in particular room parameters impacts the uncertain occupant exposures. To demonstrate the utility of this method, we simulate contaminant dispersion for a variety of possible source locations. We compute breathing-zone exposure during the releasing (source is active) and decaying (source is removed) periods. Using CFD methods, we found after a 30 minutes release the average standard deviation in the spatial distribution of exposure was approximately 28% of the source average exposure, whereas variability in the different average exposures was lower, only 10% of the total average. We also find that although uncertainty in the source location leads to variability in the average magnitude of transient exposure, it does not have a particularly large influence on the spatial distribution during the decaying period, or on the average contaminant removal rate. By systematically characterizing a room's average concentration, its variability, and the spatial variability within the room important insights can be gained as to how much uncertainty is introduced into occupant exposure predictions by assuming a uniform in-room contaminant concentration. We discuss how the results of these characterizations can improve our understanding of the uncertainty in occupant exposures relative to well-mixed models.