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建筑模拟（英文版）
建筑模拟（英文版）

双月刊

1996-3599

建筑模拟（英文版）/Journal Building SimulationCSCD北大核心EISCI
正式出版
收录年代

    Influence of indoor airflow on airborne disease transmission in a classroom

    Mojtaba ZabihiRi LiJoshua Brinkerhoff
    355-370页
    查看更多>>摘要：It has been widely accepted that the most effective way to mitigate airborne disease transmission in an indoor space is to increase the ventilation airflow,measured in air change per hour(ACH).However,increasing ACH did not effectively prevent the spread of COVID-19.To better understand the role of ACH and airflow large-scale patterns,a comprehensive fully transient computational fluid dynamics(CFD)simulation of two-phase flows based on a discrete phase model(DPM)was performed in a university classroom setting with people present.The investigations encompass various particle sizes,ventilation layouts,and flow rates.The findings demonstrated that the particle size threshold at which particles are deemed airborne is highly influenced by the background flow strength and large-scale flow pattern,ranging from 5 μm to 10 μm in the cases investigated.The effects of occupants are significant and must be precisely accounted for in respiratory particle transport studies.An enhanced ventilation design(UFAD-CDR)for university classrooms is introduced that places a premium on mitigating airborne disease spread.Compared to the baseline design at the same ACH,this design successfully reduced the maximum number density of respiratory particles by up to 85％.A novel airflow-related parameter,Horizontality,is introduced to quantify and connect the large-scale airflow pattern with indoor aerosol transport.This underscores that ACH alone cannot ensure or regulate air quality.In addition to the necessary ACH for air exchange,minimizing horizontal bulk motion is essential for reducing aerosol transmissibility within the room.
      原文链接:
    • 万方数据
    • 维普

    Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network

    Rouhui WuYizhu RenMengying TanLei Nie...
    371-386页
    查看更多>>摘要：Accurate fault diagnosis of heating,ventilation,and air conditioning(HVAC)systems is of significant importance for maintaining normal operation,reducing energy consumption,and minimizing maintenance costs.However,in practical applications,it is challenging to obtain sufficient fault data for HVAC systems,leading to imbalanced data,where the number of fault samples is much smaller than that of normal samples.Moreover,most existing HVAC system fault diagnosis methods heavily rely on balanced training sets to achieve high fault diagnosis accuracy.Therefore,to address this issue,a composite neural network fault diagnosis model is proposed,which combines SMOTETomek,multi-scale one-dimensional convolutional neural networks(M1DCNN),and support vector machine(SVM).This method first utilizes SMOTETomek to augment the minority class samples in the imbalanced dataset,achieving a balanced number of faulty and normal data.Then,it employs the M1DCNN model to extract feature information from the augmented dataset.Finally,it replaces the original Softmax classifier with an SVM classifier for classification,thus enhancing the fault diagnosis accuracy.Using the SMOTETomek-M1 DCNN-SVM method,we conducted fault diagnosis validation on both the ASHRAE RP-1043 dataset and experimental dataset with an imbalance ratio of 1:10.The results demonstrate the superiority of this approach,providing a novel and promising solution for intelligent building management,with accuracy and F1 scores of 98.45％and 100％for the RP-1043 dataset and experimental dataset,respectively.
      原文链接:
    • 万方数据
    • 维普

    Study of the multi-physics field-coupled model of the two-stage electrostatic precipitator

    Wenjia HaoYu GuoYukun WangTao Yu...
    387-398页
    查看更多>>摘要：The two-stage electrostatic precipitator is widely used to purify oil mist particles.However,there is limited research on the influences of relative humidity,particle deposition characteristics,and the generation of gaseous pollutants.Therefore,this paper established a numerical model of the electrostatic oil mist purifier and applied it to a two-stage electrostatic precipitator.Then the model was used to investigate the corona discharge characteristics under different relative humidity conditions in the charged zone,the particle deposition characteristics,the purification efficiency,the ozone concentration distribution,and the oil vapor concentration distribution in the collection zone.The results indicate that,with a constant temperature,the corona current decreases as relative humidity increase,and there is a quadratic relationship between relative humidity and current.The variation in relative humidity has little impact on the purification efficiency.The maximum ozone concentration occurs near the electrode line,and its concentration is influenced by the discharge current and inlet airflow velocity.The oil vapor concentration reaches its maximum value at the side plates,with a value of 19 ppb,while it reaches the minimum value at the collecting zone electrode plate,with a value of 2 ppb.The temperature is the main factor affecting the volatilization of the oil film,with higher temperatures resulting in higher oil vapor.
      原文链接:
    • 万方数据
    • 维普

    Deep learning to develop zero-equation based turbulence model for CFD simulations of the built environment

    Giovanni CalzolariWei Liu
    399-414页
    查看更多>>摘要：This study aims to improve the accuracy and speed of predictions for thermal comfort and air quality in built environments by creating a coupled framework between computational fluid dynamics(CFD)simulations and deep learning models.The coupling approach is showcased by the development of a data-driven turbulence model.The new turbulence model is built using a deep learning neural network,whose mapping structure is based on a zero-equation turbulence model for built environment simulations,and is coupled with the CFD software OpenFOAM to create a hybrid framework.The neural network is a standard shallow multi-layer perceptron.The number of hidden layers and nodes per layer was optimized using Bayesan optimization algorithm.The framework is trained on an indoor environment case study,as well as tested on an indoor office simulation and an outdoor building array simulation.Results show that the deep learning based turbulence model is more robust and faster than traditional two-equation Reynolds average Navier-Stokes(RANS)turbulence models,while maintaining a similar level of accuracy.The model also outperforms the standard algebraic zero-equation model due to its superior ability to generalize to various flow scenarios.Despite some challenges,namely the mapping constraint,the limited training dataset size and the source of generation of training data,the hybrid framework demonstrates the viability of the coupling technique and serves as a starting point for future development of more reliable and advanced models.
      原文链接:
    • 万方数据
    • 维普

    High-performance formaldehyde prediction for indoor air quality assessment using time series deep learning

    Liu LuXinyu HuangXiaojun ZhouJunfei Guo...
    415-429页
    查看更多>>摘要：Indoor air pollution resulting from volatile organic compounds(VOCs),especially formaldehyde,is a significant health concern needed to predict indoor formaldehyde concentration(Cf)in green intelligent building design.This study develops a thermal and wet coupling calculation model of porous fabric to account for the migration of formaldehyde molecules in indoor air and cotton,silk,and polyester fabric with heat flux in Harbin,Beijing,Xi'an,Shanghai,Guangzhou,and Kunming,China.The time-by-time indoor dry-bulb temperature(T),relative humidity(RH),and Cf,obtained from verified simulations,were collated and used as input data for the long short-term memory(LSTM)of the deep learning model that predicts indoor multivariate time series Cf from the secondary source effects of indoor fabrics(adsorption and release of formaldehyde).The trained LSTM model can be used to predict multivariate time series Cf at other emission times and locations.The LSTM-based model also predicted Cf with mean absolute percentage error(MAPE),symmetric mean absolute percentage error(SMAPE),mean absolute error(MAE),mean square error(MSE),and root mean square error(RMSE)that fell within 10％,10％,0.5,0.5,and 0.8,respectively.In addition,the characteristics of the input dataset,model parameters,the prediction accuracy of different indoor fabrics,and the uncertainty of the data set are analyzed.The results show that the prediction accuracy of single data set input is higher than that of temperature and humidity input,and the prediction accuracy of LSTM is better than recurrent neural network(RNN).The method's feasibility was established,and the study provides theoretical support for guiding indoor air pollution control measures and ensuring human health and safety.
      原文链接:
    • 万方数据
    • 维普

    Reducing children's exposure to di(2-ethylhexyl)phthalate in homes and kindergartens in China:Impact on lifetime cancer risks and burden of disease

    Dongsheng TaoWen SunDonghui MoYonghui Lin...
    431-440页
    查看更多>>摘要：Exposure to di(2-ethylhexyl)phthalate(DEHP)in the indoor environment has been linked with significant health risks for Chinese children.Multi-phase DEHP concentrations in Chinese residences and kindergartens were estimated using a mass balance model based on the current baseline condition and control strategies(i.e.,increasing ventilation rate,reducing area of sources,using mechanical ventilation systems,and using portable air cleaners).The health benefits of each control strategy were quantified as the reduction in lifetime cancer risks(LCR)and burden of disease(BoD).In the current situation,the mean LCR and disability-adjusted life years(DALY)number attributable to indoor DEHP exposure for Chinese children were around 6.0x10-6 and 155 thousand,respectively.The mean LCR and DALY might be reduced by 25％-54％and 16％-40％,respectively,by increasing air exchange rates by 100％,reducing the use of source materials by two-thirds or deploying commercial air cleaners in naturally ventilated buildings.Meanwhile,avoidable DALYs could result in a reduction of mean economic losses of 2.2-5.3 billion RMB.Mechanical ventilation systems with filtration units may not be helpful for reducing children's health risks.House-specific and tailor-made control measures are critical in lowering indoor exposure to DEHP to promote sustainable buildings and children's health in China.
      原文链接:
    • 万方数据

    A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

    Mohammad Kaosain AkbarManar AmayriNizar Bouguila
    441-457页
    查看更多>>摘要：Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers'appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15％improved overall improved F1 micro score and almost 26％improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compared to the benchmarking techniques that also used semi-supervised learning approach.
      原文链接:
    • 万方数据
    • 维普

    Assessing the energy saving potential of using adaptive setpoint temperatures:The case study of a regional adaptive comfort model for Brazil in both the present and the future

    Daniel Sánchez-GarcíaDavid Bienvenido-HuertasCarlos Rubio-BellidoRicardo Forgiarini Rupp...
    459-482页
    查看更多>>摘要：It has been found in recent years that using setpoint temperatures based on adaptive thermal comfort models is a successful method of energy conservation.Recent studies using adaptive setpoint temperatures incorporate international models from ASHRAE Standard 55 and EN16798-1.This study,however,has instead considered a regional Brazilian adaptive comfort model.This study investigates the energy demand arising from the use of a local Brazilian comfort model in order to assess the energy implications from the use of the worldwide ASHRAE Standard 55 adaptive model and various fixed setpoint temperatures.All of Brazil's climate zones,full air-conditioning,mixed-mode building operating modes,present-day climate change scenarios,and future scenarios-specifically Representative Concentration Pathways(RCP)2.6,4.5,and 8.5 for the years 2050 and 2100-have all been taken into account in building energy simulations.The use of adaptive setpoint temperatures based on the Brazilian local model considering mixed-mode has been found to significantly reduce energy consumption when compared to static setpoint temperatures(average energy-saving values ranging from 52％to 58％)and the ASHRAE 55 adaptive model(average values ranging from 15％to 21％).Considering climate change and the mixed-mode Brazilian model,the overall energy demand for the three groups of climatic zones(annual average outdoor temperatures ≤21 ℃,＞21 and ≤ 25 ℃ and＞25 ℃)ranged between 2％decrease and 5％increase,4％and 27％increase,and 13％and 45％increase,respectively.It is concluded as a consequence that setting setpoint temperatures based on the Brazilian local adaptive comfort model is a very efficient energy-saving method.
      原文链接:
    • 万方数据
    • 维普

    Mechanistic modeling of copper corrosions in data center environments

    Rui ZhangJianshun ZhangRoger SchmidtJeremy L.Gilbert...
    483-492页
    查看更多>>摘要：Air-side economizers are increasingly used to take advantage of"free-cooling"in data centers with the intent of reducing the carbon footprint of buildings.However,they can introduce outdoor pollutants to indoor environment of data centers and cause corrosion damage to the information technology equipment.To evaluate the reliability of information technology equipment under various thermal and air-pollution conditions,a mechanistic model based on multi-ion transport and chemical reactions was developed.The model was used to predict Cu corrosion caused by Cl2-containing pollutant mixtures.It also accounted for the effects of temperature(25 ℃ and 28 ℃),relative humidity(50％,75％,and 95％),and synergism.It also identified higher air temperature as a corrosion barrier and higher relative humidity as a corrosion accelerator,which agreed well with the experimental results.The average root mean square error of the prediction was 13.7 Å.The model can be used to evaluate the thermal guideline for data centers design and operation when Cl2 is present based on pre-established acceptable risk of corrosion in data centers'environment.
      原文链接:
    • 万方数据
    • 维普

    CBE Clima Tool:A free and open-source web application for climate analysis tailored to sustainable building design

    Giovanni BettiFederico TartariniChristine NguyenStefano Schiavon...
    493-508页
    查看更多>>摘要：Climate-responsive building design holds immense potential for enhancing comfort,energy efficiency,and environmental sustainability.However,many social,cultural,and economic obstacles might prevent the wide adoption of designing climate-adapted buildings.One of these obstacles can be removed by enabling practitioners to easily access,visualize and analyze local climate data.The CBE Clima Tool(Clima)is a free and open-source web application that offers easy access to publicly available weather files and has been created for building energy simulation and design.It provides a series of interactive visualizations of the variables contained in the EnergyPlus Weather Files and several derived ones like the UTCI or the adaptive comfort indices.It is aimed at students,educators,and practitioners in the architecture and engineering fields.Since its inception,Clima's user base has exhibited robust growth,attracting over 25,000 unique users annually from across 70 countries.Our tool is poised to revolutionize climate-adaptive building design,transcending geographical boundaries and fostering innovation in the architecture and engineering fields.
      原文链接:
    • 万方数据
    • 维普