Building Simulation最新文献

People in elevators are at risk of respiratory infection because the elevator cabin is crowded and has poor ventilation. The exhaled particles may be inhaled by the susceptible person, deposited on the surface and suspended in the elevator, which can result in direct and indirect transmission. However, whether the air vent designs adopted in the elevator can effectively reduce the transmission risk of respiratory particles remains unknown. In this study, the dispersion of particles under four common ventilation strategies used in the commercial elevator was investigated by proven computational fluid dynamics (CFD) simulations. The flow field was simulated with the RNG k-ξ turbulence model and the Lagrangian method was adopted to track particle trajectories. The effects of air vent layout and airflow rate on particle transmission were analyzed. We found that more than 50% of exhaled particles (average value) were suspended in the cabin and difficult to discharge under the investigated ventilation strategies. The deposited fraction of particles on the susceptible person reached up to 39.14% for infiltration ventilation, which led to a high risk of contact infection. Increasing the ventilation rate could not significantly reduce the inhalation proportion of particles due to the poor airflow distribution inside the elevator. A more proper ventilation strategy should be explored for the elevator to control transmission risk.

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.

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-M1DCNN-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.

During the initial phases of operation following the construction or renovation of existing buildings, the availability of historical power usage data is limited, which leads to lower accuracy in load forecasting and hinders normal usage. Fortunately, by transferring load data from similar buildings, it is possible to enhance forecasting accuracy. However, indiscriminately expanding all source domain data to the target domain is highly likely to result in negative transfer learning. This study explores the feasibility of utilizing similar buildings (source domains) in transfer learning by implementing and comparing two distinct forms of multi-source transfer learning. Firstly, this study focuses on the Higashita area in Kitakyushu City, Japan, as the research object. Four buildings that exhibit the highest similarity to the target buildings within this area were selected for analysis. Next, the two-stage TrAdaBoost.R2 algorithm is used for multi-source transfer learning, and its transfer effect is analyzed. Finally, the application effects of instance-based (IBMTL) and feature-based (FBMTL) multi-source transfer learning are compared, which explained the effect of the source domain data on the forecasting accuracy in different transfer modes. The results show that combining the two-stage TrAdaBoost.R2 algorithm with multi-source data can reduce the CV-RMSE by 7.23% compared to a single-source domain, and the accuracy improvement is significant. At the same time, multi-source transfer learning, which is based on instance, can better supplement the integrity of the target domain data and has a higher forecasting accuracy. Overall, IBMTL tends to retain effective data associations and FBMTL shows higher forecasting stability. The findings of this study, which include the verification of real-life algorithm application and source domain availability, can serve as a theoretical reference for implementing transfer learning in load forecasting.

Abstract

The morphology of urban areas plays a crucial role in determining solar potential, which directly affects photovoltaic capacity and the achievement of net-zero outcomes. This study focuses on the City of Melbourne to investigate the utilization of solar energy across different urban densities and proposes optimized morphologies. The analysis encompasses blocks with diverse population densities, examining medium and high-density areas. By utilizing a multi-objective genetic optimization approach, the urban morphology of these blocks is refined. The findings indicate that low-density blocks exhibit photovoltaic potential ranging from 1 to 6.6 times their total energy consumption. Medium and high-density blocks achieve photovoltaic potential levels approximately equivalent to 40%–85% of their overall energy consumption. Moreover, significant variations in photovoltaic potential are observed among different urban forms within medium and high-density blocks. An “elevated corners with central valley” prototype is proposed as an effective approach, enhancing the overall photovoltaic potential by approximately 14%. This study introduces novel analytical concepts, shedding light on the intricate relationship between urban morphologies and photovoltaic potential.

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 6.0×10⁻⁶ 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.

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.

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 °C and 28 °C), 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.

Abstract

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.

Indoor air pollution resulting from volatile organic compounds (VOCs), especially formaldehyde, is a significant health concern needed to predict indoor formaldehyde concentration (C_f) 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 C_f, 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 C_f 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 C_f 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.