A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

  Subjects -> STATISTICS (Total: 130 journals)
The end of the list has been reached or no journals were found for your choice.
Similar Journals
Journal Cover
Building Simulation
Journal Prestige (SJR): 0.839
Citation Impact (citeScore): 2
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1996-8744 - ISSN (Online) 1996-3599
Published by Springer-Verlag Homepage  [2468 journals]
  • Deep learning to develop zero-equation based turbulence model for CFD
           simulations of the built environment

    • Free pre-print version: Loading...

      Abstract: 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.
      PubDate: 2024-03-01
       
  • Mechanistic modeling of copper corrosions in data center environments

    • Free pre-print version: Loading...

      Abstract: Abstract 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.
      PubDate: 2024-03-01
       
  • CBE Clima Tool: A free and open-source web application for climate
           analysis tailored to sustainable building design

    • Free pre-print version: Loading...

      Abstract: Abstract 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.
      PubDate: 2024-03-01
       
  • A novel non-intrusive load monitoring technique using semi-supervised deep
           learning framework for smart grid

    • Free pre-print version: Loading...

      Abstract: Abstract 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 F1micro 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.
      PubDate: 2024-03-01
       
  • Study of the multi-physics field-coupled model of the two-stage
           electrostatic precipitator

    • Free pre-print version: Loading...

      Abstract: Abstract 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.
      PubDate: 2024-03-01
       
  • 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

    • Free pre-print version: Loading...

      Abstract: Abstract 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 °C, > 21 and ≤ 25 °C and > 25 °C) 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.
      PubDate: 2024-03-01
       
  • High-performance formaldehyde prediction for indoor air quality assessment
           using time series deep learning

    • Free pre-print version: Loading...

      Abstract: Abstract 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.
      PubDate: 2024-03-01
       
  • Reducing children’s exposure to di(2-ethylhexyl) phthalate in homes and
           kindergartens in China: Impact on lifetime cancer risks and burden of
           disease

    • Free pre-print version: Loading...

      Abstract: Abstract 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.
      PubDate: 2024-03-01
       
  • Evaluating different levels of information on the calibration of building
           energy simulation models

    • Free pre-print version: Loading...

      Abstract: Abstract A poorly calibrated model undermines confidence in the effectiveness of building energy simulation, impeding the widespread application of advanced energy conservation measures (ECMs). Striking a balance between information-gathering efforts and achieving sufficient model credibility is crucial but often obscured by ambiguities. To address this gap, we model and calibrate a test bed with different levels of information (LOI). Beginning with an initial model based on building geometry (LOI 1), we progressively introduce additional information, including nameplate information (LOI 2), envelope conductivity (LOI 3), zone infiltration rate (LOI 4), AHU fan power (LOI 5), and HVAC data (LOI 6). The models are evaluated for accuracy, consistency, and the robustness of their predictions. Our results indicate that adding more information for calibration leads to improved data fit. However, this improvement is not uniform across all observed outputs due to identifiability issues. Furthermore, for energy-saving analysis, adding more information can significantly affect the projected energy savings by up to two times. Nevertheless, for ECM ranking, models that did not meet ASHRAE 14 accuracy thresholds can yield correct retrofit decisions. These findings underscore equifinality in modeling complex building systems. Clearly, predictive accuracy is not synonymous with model credibility. Therefore, to balance efforts in information-gathering and model reliability, it is crucial to (1) determine the minimum level of information required for calibration compatible with its intended purpose and (2) calibrate models with information closely linked to all outputs of interest, particularly when simultaneous accuracy for multiple outputs is necessary.
      PubDate: 2024-02-24
       
  • The use of green infrastructure and irrigation in the mitigation of urban
           heat in a desert city

    • Free pre-print version: Loading...

      Abstract: Abstract Severe urban heat, a prevalent climate change consequence, endangers city residents globally. Vegetation-based mitigation strategies are commonly employed to address this issue. However, the Middle East and North Africa are under investigated in terms of heat mitigation, despite being one of the regions most vulnerable to climate change. This study assesses the feasibility and climatic implications of wide-scale implementation of green infrastructure (GI) for heat mitigation in Riyadh, Saudi Arabia—a representative desert city characterized by low vegetation coverage, severe summer heat, and drought. Weather research forecasting model (WRF) is used to simulate GI cooling measures in Riyadh’s summer condition, including measures of increasing vegetation coverage up to 60%, considering irrigation and vegetation types (tall/short). In Riyadh, without irrigation, increasing GI fails to cool the city and can even lead to warming (0.1 to 0.3 °C). Despite irrigation, Riyadh’s overall GI cooling effect is 50% lower than GI cooling expectations based on literature meta-analyses, in terms of average peak hour temperature reduction. The study highlights that increased irrigation substantially raises the rate of direct soil evaporation, reducing the proportion of irrigation water used for transpiration and thus diminishing efficiency. Concurrently, water resource management must be tailored to these specific considerations.
      PubDate: 2024-02-24
       
  • Utilizing interpretable stacking ensemble learning and NSGA-III for the
           prediction and optimisation of building photo-thermal environment and
           energy consumption

    • Free pre-print version: Loading...

      Abstract: Abstract This study develops an approach consisting of a stacking model integrated with a multi-objective optimisation algorithm aimed at predicting and optimising the ecological performance of buildings. The integrated model consists of five base models and a meta-model, which significantly improves the prediction performance. Specifically, the R2 value was improved by 9.19% and the error metrics MAE, MSE, MAPE, and CVRMSE were reduced by 69.47%, 79.88%, 67.32%, and 57.02%, respectively, compared to the single prediction model. According to the research on interpretable machine learning, adding the SHAP value gives us a deeper understanding of the impact of each architectural design parameter on the performance. In the multi-objective optimisation part, we used the NSGA-III algorithm to successfully improve the energy efficiency, daylight utilisation and thermal comfort of the building. Specifically, the optimal design solution reduces the energy use intensity by 31.6 kWh/m2, improves the useful daylight index by 39%, and modulated the thermal comfort index, resulting in a decrement of 0.69 °C for the summer season and an enhancement of 0.64 °C for the winter season, respectively. Overall, this study provides building designers and decision makers with a tool to make better design decisions at an early stage to achieve a better combination of energy efficiency, daylight utilisation and thermal comfort optimisation in an integrated manner, providing an important support for achieving sustainable building design.
      PubDate: 2024-02-23
       
  • An innovative heterogeneous transfer learning framework to enhance the
           scalability of deep reinforcement learning controllers in buildings with
           integrated energy systems

    • Free pre-print version: Loading...

      Abstract: Abstract Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.
      PubDate: 2024-02-20
       
  • The recirculation flow after different cross-section shaped high-rise
           buildings with applications to ventilation assessment and drag
           parameterization

    • Free pre-print version: Loading...

      Abstract: Abstract The building cross-section shape significantly affects the flow characteristics around buildings, especially the recirculation region behind the high-rise building. Eight generic building shapes including square, triangle, octagon, T-shaped, cross-shaped, #-shaped, H-shaped and L-shaped are examined to elucidate their effects on the flow patterns, recirculation length L and areas A using computational fluid dynamics (CFD) simulations with Reynolds-averaged Navier-Stokes (RANS) approach. The sizes and positions of the vortexes behind the buildings are found to be substantially affected by the building shapes and subsequently changing the recirculation flows. The recirculation length L is in the range of 1.6b–2.6b with an average of 2b. The maximum L is found for L-shaped building (2.6b) while the shortest behind octagon building (1.6b). The vertical recirculation area Av is in the range of 1.5b2–3.2b2 and horizontal area Ah in 0.9b2–2.2b2. The L, Av and Ah generally increase with increasing approaching frontal area when the wind direction changes but subject to the dent structures of the #-shaped and cross-shaped buildings. The area-averaged wind velocity ratio (AVR), which is proposed to assess the ventilation performance, is in the range of 0.05 and 0.14, which is around a three-fold difference among the different building shapes. The drag coefficient parameterized by Ah varies significantly, suggesting that previous models without accounting for building shape effect could result in large uncertainty in the drag predictions. These findings provide important reference for improving pedestrian wind environment and shed some light on refining the urban canopy parameterization by considering the building shape effect.
      PubDate: 2024-02-20
       
  • Experimental study on the CO2 concentration and age of air distribution
           inside tiny sleeping spaces

    • Free pre-print version: Loading...

      Abstract: Abstract In recent years, rapid urban development has led to capsule hotels, sleep pods, and other tiny sleeping spaces that adapt to people’s fast-paced lives, achieving maximum functionality with a very small footprint. However, due to the small space, human metabolic pollutant (such as CO2) is more likely to accumulate, and the air is not easily circulated. In this paper, a full-size experimental platform is set up with three types of ventilation modes to explore the exclusion efficiency of metabolic pollutants and the overall distribution of age of air under these ventilation modes. The conclusions showed that the mean values of metabolic pollutant exclusion rates for the different ventilation modalities varied very little across the spatial dimensions of the confined space but varied considerably in the area around the head. The double-side attached ventilation method was the most effective in removing human metabolic pollutants, especially in the head region (CN ≥ 0.92), while the single-wall attached ventilation method had the best air exchange efficiency (η ≥ 0.85). This suggests an inconsistent distribution of CO2 and age of air, which is contrary to general common sense. The conclusions of this paper can guide the design of ventilation for tiny sleeping spaces.
      PubDate: 2024-02-19
       
  • Potential application of radiant floor cooling systems for residential
           buildings in different climate zones

    • Free pre-print version: Loading...

      Abstract: Abstract A radiant floor cooling system (RFCS) is a high-comfort and low energy consumption system suitable for residential buildings. Radiant floor systems usually work with fresh air, and their operating performance is affected by climatic conditions. Indoor and outdoor environmental disturbances and the system’s control strategy affect the indoor thermal comfort and energy efficiency of the system. Firstly, a multi-story residential building model was established in this study. Transient system simulation program was used to study the operation dynamics of three control strategies of the RFCS based on the calibrated model. Then, the performance of the control strategies in five climate zones in China were compared using multi-criteria decision-making in combination. The results show that control strategy has a negligible effect on condensation risk, but the thermal comfort and economic performance differ for different control strategies. The adaptability of different control strategies varies in different climate zones based on the consideration of multiple factors. The performance of the direct-ground cooling source system is better in Hot summer and warm winter zone. The variable air volume control strategy scores higher in Serve cold and Temperate zones, and the hours exceeding thermal comfort account for less than 3% of the total simulation period. Therefore, it is suggested to choose the RFCS control strategy for residential buildings according to the climate zone characteristics, to increase the energy savings. Our results provide a reliable reference for implementing RFCSs in residential buildings.
      PubDate: 2024-02-12
       
  • Seasonal thermal energy storage using natural structures: GIS-based
           potential assessment for northern China

    • Free pre-print version: Loading...

      Abstract: Abstract Seasonal thermal energy storage (STES) allows storing heat for long-term and thus promotes the shifting of waste heat resources from summer to winter to decarbonize the district heating (DH) systems. Despite being a promising solution for sustainable energy system, large-scale STES for urban regions is lacking due to the relatively high initial investment and extensive land use. To close the gap, this study assesses the potentials of using two naturally available structures for STES, namely valley and ground pit sites. Based on geographical information system (GIS) methods, the available locations are searched from digital elevation model and selected considering several criteria from land uses and construction difficulties. The costs of dams to impound the reservoir and the yielded storage capacities are then quantified to guide the choice of suitable sites. The assessment is conducted for the northern China where DH systems and significant seasonal differences of energy demand exist. In total, 2,273 valley sites and 75 ground pit sites are finally identified with the energy storage capacity of 15.2 billion GJ, which is much larger than the existing DH demand in northern China. The results also prove that 682 valley sites can be achieved with a dam cost lower than 20 CNY/m3. By conducting sensitivity analysis on the design dam wall height and elevations, the choices of available natural structures are expanded but practical issues about water pressures and constructions are also found. Furthermore, the identified sites are geographically mapped with nearest urban regions to reveal their roles in the DH systems. In general, 560 urban regions are found with potential STES units and most of them have STES storage capacities larger than their own DH demand. The novel planning methodology of this study and publicly available datasets create possibilities for the implementations of large-scale STES in urban DH systems.
      PubDate: 2024-02-12
       
  • Identification of rural courtyards’ utilization status using deep
           learning and machine learning methods on unmanned aerial vehicle images in
           north China

    • Free pre-print version: Loading...

      Abstract: Abstract The issue of unoccupied or abandoned homesteads (courtyards) in China emerges given the increasing aging population, rapid urbanization and massive rural-urban migration. From the aspect of rural vitalization, land-use planning, and policy making, determining the number of unoccupied courtyards is important. Field and questionnaire-based surveys were currently the main approaches, but these traditional methods were often expensive and laborious. A new workflow is explored using deep learning and machine learning algorithms on unmanned aerial vehicle (UAV) images. Initially, features of the built environment were extracted using deep learning to evaluate the courtyard management, including extracting complete or collapsed farmhouses by Alexnet, detecting solar water heaters by YOLOv5s, calculating green looking ratio (GLR) by FCN. Their precisions exceeded 98%. Then, seven machine learning algorithms (Adaboost, binomial logistic regression, neural network, random forest, support vector machine, decision trees, and XGBoost algorithms) were applied to identify the rural courtyards’ utilization status. The Adaboost algorithm showed the best performance with the comprehensive consideration of most metrics (Accuracy: 0.933, Precision: 0.932, Recall: 0.984, F1-score: 0.957). Results showed that identifying the courtyards’ utilization statuses based on the courtyard built environment is feasible. It is transferable and cost-effective for large-scale village surveys, and may contribute to the intensive and sustainable approach to rural land use.
      PubDate: 2024-02-02
       
  • Study on the performance of lightweight roadway wall thermal insulation
           coating containing EP-GHB mixed ceramsite

    • Free pre-print version: Loading...

      Abstract: Abstract As the mining depth increases, the problem of high-temperature thermal damage mainly caused by heat dissipation of surrounding rock is becoming more and more obvious. It is very important to solve the environmental problem of mine heat damage to improve the efficiency of mineral resource exploitation and protect the physical and mental health of workers. One can apply thermal insulation coating on the walls of mine roadways as a means of implementing active heat insulation. In this paper, expanded perlite (EP) and glazed hollow bead (GHB) are used as the main thermal insulation materials, ceramsite and sand as aggregate, plus glass fiber and sodium dodecyl sulfate to develop a new lightweight composite thermal insulation coating through orthogonal experiment method. According to the plate heat flow meter method and mechanical test method, the thermal insulation and mechanical properties of EP-GHB mixed ceramsite coating were studied by making specimens with different parameter ratios, and according to the analysis of the experimental results, the optimal mix ratio of the coating was selected. In addition, Fluent numerical simulation software was used to establish the roadway model, and the thermal insulation effect of the coating in the roadway under different working conditions was studied. The results show that the thermal conductivity of the prepared composite thermal insulation coating material is only 8.5% of that of ordinary cement mortar, and the optimal thickness of adding thermal insulation coating is 0.2 m, which can reduce the outlet air temperature of the roadway with a length of 1000 m by 4.87 K at this thickness. The thermal insulation coating developed in this study has the advantages of simple technology and strong practicability, and has certain popularization and application value in mine heat damage control.
      PubDate: 2024-02-02
       
  • Influence of indoor airflow on airborne disease transmission in a
           classroom

    • Free pre-print version: Loading...

      Abstract: Abstract 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.
      PubDate: 2024-01-19
       
  • Fault diagnosis of HVAC system with imbalanced data using multi-scale
           convolution composite neural network

    • Free pre-print version: Loading...

      Abstract: Abstract 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.
      PubDate: 2024-01-13
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 44.192.20.240
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-
JournalTOCs
 
 

 A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

  Subjects -> STATISTICS (Total: 130 journals)
The end of the list has been reached or no journals were found for your choice.
Similar Journals
Similar Journals
HOME > Browse the 73 Subjects covered by JournalTOCs  
SubjectTotal Journals
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 44.192.20.240
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-