Query：
学者姓名：张伟荣
Refining：
Year
Type
Indexed by
Source
Complex
Co-Author
Language
Clean All
Abstract ：
Indoor airflow distribution significantly influences temperature regulation, humidity control, and pollutant dispersion, directly impacting thermal comfort and occupant health. Accurate and efficient prediction of airflow fields is essential for optimizing ventilation systems and enabling real-time control. However, existing computational approaches for dynamic ventilation are computationally intensive and have limited generalization capabilities. This study leverages the Fourier neural operator (FNO), a method rooted in operator learning and Fourier transform principles, to develop a three-dimensional (3D) airflow simulation model capable of predicting velocity and its components. The model was trained using 200 s of sinusoidal ventilation data (amplitude: 0.4) and evaluated under diverse air supply patterns, including sinusoidal (amplitude: 0.8), intermittent, and stepwise periodic ventilation. Additionally, the model's performance was assessed with low-resolution training data and further tested for recursive prediction accuracy. Results reveal that the FNO method achieves high accuracy, with a mean square error of 9.906 x 10-5 for sinusoidal amplitude 0.8 and 4.004 x 10-5 over 400 time steps for sinusoidal, intermittent, and stepwise conditions. Further evaluations, including tests on low-resolution training data and recursive prediction, were conducted to examine the model's resolution invariance and assess its performance in iterative forecasting. These findings demonstrate the FNO method's potential for robust, efficient prediction of 3D unsteady airflow fields, providing a pathway for real-time ventilation system optimization.
Keyword ：
computational fluid dynamics computational fluid dynamics rapid prediction rapid prediction Fourier neural operator Fourier neural operator velocity field velocity field
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ding, Xiaoxiao , Zhang, Haotian , Zhang, Weirong et al. A Fourier neural operator-based method for rapid prediction of 3D indoor airflow dynamics [J]. | BUILDING SIMULATION , 2025 . |
| MLA | Ding, Xiaoxiao et al. "A Fourier neural operator-based method for rapid prediction of 3D indoor airflow dynamics" . | BUILDING SIMULATION (2025) . |
| APA | Ding, Xiaoxiao , Zhang, Haotian , Zhang, Weirong , Zhang, Weijia , Xuan, Yingli . A Fourier neural operator-based method for rapid prediction of 3D indoor airflow dynamics . | BUILDING SIMULATION , 2025 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Wearable ventilation devices can reduce respiratory exposure in polluted environments. This study investigates a neck-side ventilation system using Computational Fluid Dynamics (CFD) simulations and experimental validation. The system delivers clean air through dual neck vents, deflecting along the face under the Coanda effect to form a clean air layer around the nose and mouth, reducing exposure to pollutants. Key factors, including roll angles, inlet pitch angles, and air speeds, were analysed for their impact on pollutant exposure reduction (PER). Results show that at roll angles of 30 degrees or 45 degrees, the Coanda effect deflects and converges the airflow in front of the face, forming a protective air layer. The pitch angle affects the convergence point, with 40 degrees and 45 degrees angles optimising the clean air layer's position around the breathing zone. Airflow velocity has a secondary impact when optimal roll and pitch angles are chosen. However, in suboptimal combinations, higher airflow velocities improve pollutant shielding, except when the roll angle is 0 degrees, where higher speeds worsen pollutant entrainment into the breathing zone. The system achieves a maximum PER of 75.2 % at a roll angle of 30 degrees and a pitch angle of 45 degrees This study confirms the potential of neck-side ventilation for respiratory protection and provides guidance for optimising design parameters to improve performance in polluted environments.
Keyword ：
Computational fluid dynamics Computational fluid dynamics Wearable ventilation devices Wearable ventilation devices Pollutant exposure reduction Pollutant exposure reduction Coanda effect Coanda effect Neck-side ventilation Neck-side ventilation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Haotian , Zhang, Weirong , Ding, Xiaoxiao et al. Study of a novel neck-side ventilation system for reducing pollutant exposure [J]. | BUILDING AND ENVIRONMENT , 2025 , 277 . |
| MLA | Zhang, Haotian et al. "Study of a novel neck-side ventilation system for reducing pollutant exposure" . | BUILDING AND ENVIRONMENT 277 (2025) . |
| APA | Zhang, Haotian , Zhang, Weirong , Ding, Xiaoxiao , Xuan, Yingli . Study of a novel neck-side ventilation system for reducing pollutant exposure . | BUILDING AND ENVIRONMENT , 2025 , 277 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Predicting transient particle transport is crucial to address the risks posed to human health by indoor contaminants and to improve the design and control of ventilation systems. The Markov chain model with a coarse matrix has proven to be efficient in this regard, demonstrating faster speeds than traditional methods and higher computational accuracy when based on non-uniform states. However, its application under unsteady airflow conditions is limited, and discussions of how to determine non-uniform Markov states and their corresponding transient transition probabilities when the flow field changes are limited. Therefore, this study developed an application method of Markov chain model based on fixed non-uniform states in unsteady airflow fields. First, this model rapidly provides various Markov state schemes based on airflow field velocity and clustering methods. It then selects the optimal scheme through frequency and finally obtains the transition matrix through Lagrangian tracking for particle transport prediction. This model reduces the computational cost of transient particle transport when the wind speed changes while ensuring high computational accuracy. The proposed method was validated using published flow field experiments and simulation data. The computational speed of the proposed method was compared with computational fluid dynamics (CFD), and in the case studies considered in this research, the normalized root-mean-square deviation of the model was less than 10% and the total computation time was more than 75% faster than that of CFD.
Keyword ：
Periodic ventilation Periodic ventilation Computational fluid dynamics Computational fluid dynamics Non-uniform states Non-uniform states Contaminant transport Contaminant transport Markov chain model Markov chain model
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ding, Xiaoxiao , Zhang, Haotian , Zhang, Weirong et al. Rapid prediction of transient particle transport under periodic ventilation using a non-uniform state Markov chain model [J]. | ENERGY AND BUILDINGS , 2024 , 321 . |
| MLA | Ding, Xiaoxiao et al. "Rapid prediction of transient particle transport under periodic ventilation using a non-uniform state Markov chain model" . | ENERGY AND BUILDINGS 321 (2024) . |
| APA | Ding, Xiaoxiao , Zhang, Haotian , Zhang, Weirong , Xuan, Yingli . Rapid prediction of transient particle transport under periodic ventilation using a non-uniform state Markov chain model . | ENERGY AND BUILDINGS , 2024 , 321 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Building flexible energy systems (BFES) can be enhanced by introducing storage batteries. Providing timely scheduling strategies for flexible resources can improve the system's energy utilization. BFES's scheduling strategies are often adjusted based on Intra-hour photovoltaic(PV) output. Intra-hour PV power generation can be predicted by analyzing cloud imagery data; however, this method does not meet the economic requirements of BFES due to its cost and instrumentation. Therefore, this study proposes a low-cost method for intra-hour PV power generation prediction (IHP) for BFES and explores the impact of integrating this approach into BFES on the rate of renewable energy consumption. This method combined low-quality sky images captured using fisheye cameras installed above buildings with historical electricity generation data and employed convolutional neural networks. The feasibility of the IHP method and the advantages of incorporating it into BFES were verified by applying it to a building equipped with PV devices in Changping, Beijing. The performance of the proposed model algorithm was compared with those of existing models. The proposed method achieved average prediction accuracy improvements of 25.1 and 12.5 % compared with existing models under sunny and cloudy conditions, respectively. Under clear conditions, the model could predict the PV power generation within the next 25 min, whereas under cloudy conditions, the model could predict the power generation within 10 min. In addition, integrating IHP into the scheduling strategy of BFES can improve the renewable energy consumption rate by 44.4 % on the original basis.
Keyword ：
Convolutional Neural Network Convolutional Neural Network PV PV Intra-hour PV Prediction Intra-hour PV Prediction Building Flexible Energy System Building Flexible Energy System
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Su, Yongyi , Zhang, Weirong , Deng, Gaofeng et al. An Intra-Hour photovoltaic power generation prediction method for flexible building energy systems and its application in operation scheduling strategy [J]. | SOLAR ENERGY , 2024 , 284 . |
| MLA | Su, Yongyi et al. "An Intra-Hour photovoltaic power generation prediction method for flexible building energy systems and its application in operation scheduling strategy" . | SOLAR ENERGY 284 (2024) . |
| APA | Su, Yongyi , Zhang, Weirong , Deng, Gaofeng , Wang, Zhichao . An Intra-Hour photovoltaic power generation prediction method for flexible building energy systems and its application in operation scheduling strategy . | SOLAR ENERGY , 2024 , 284 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Cities are the main contributors to carbon dioxide emissions, and communities are crucial for implementing urban low-carbon emissions. As a result of the controversies surrounding existing transformation technologies and the varying conditions faced by different targets for transformation, an effective method is required for application in specific target communities to achieve good carbon reduction effects. We propose a method that utilizes the knowledge graph and scenario analysis to systematically screen and evaluate community low-carbon technologies. First, a comprehensive knowledge map spanning multiple fields was constructed, and the correlations were analyzed using CiteSpace. Additionally, we categorized the transformation technologies into nine scenarios to assess their effectiveness and feasibility across different community contexts. Finally, the near-zero carbon emission community pilot area of Shenzhen City was selected as a case study, and the method was applied as a demonstration. The total carbon emissions of the pilot community were reduced by 43% after applying the proposed method. This method is helpful for technical screening at the low-carbon transformation stage and also provides a useful reference for the low-carbon construction stage of communities.
Keyword ：
knowledge graph knowledge graph community community Decision-making Decision-making Low-carbon transformation Low-carbon transformation scenario analysis scenario analysis
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yu, Tingjia , Bai, Yifei , Zhou, Ning et al. Decision-making method for low-carbon transformation technology in existing residential areas based on knowledge graph and scenario analysis methods [J]. | INTERNATIONAL JOURNAL OF GREEN ENERGY , 2024 , 22 (1) : 35-49 . |
| MLA | Yu, Tingjia et al. "Decision-making method for low-carbon transformation technology in existing residential areas based on knowledge graph and scenario analysis methods" . | INTERNATIONAL JOURNAL OF GREEN ENERGY 22 . 1 (2024) : 35-49 . |
| APA | Yu, Tingjia , Bai, Yifei , Zhou, Ning , Zhang, Weirong , Yang, Xiu . Decision-making method for low-carbon transformation technology in existing residential areas based on knowledge graph and scenario analysis methods . | INTERNATIONAL JOURNAL OF GREEN ENERGY , 2024 , 22 (1) , 35-49 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
In the context of indoor environmental control, users have varying preferences with respect to their ventilation demands. Currently, the design and control of ventilation schemes do not consider user preferences adequately. To optimize the ventilation strategies based on user demands, the weight coefficients of occupant ventilation demands were extracted in this study using questionnaire surveys. The distinctive characteristics of ventilation preferences among individuals were then analyzed. Computational fluid dynamics (CFD) was used to simulate ventilation schemes with multiple air supply parameters within an office environment, and a database of ventilation performance under different conditions was generated. A multi-index evaluation was conducted to determine the recommended air supply scheme. Finally, a backpropagation neural network was employed to establish a personalized recommendation model for the ventilation scheme. The model inputs include the environmental weight coefficients assigned by the occupants and objective environmental parameters. Its outputs include the ventilation performance and air supply parameters. The results demonstrate that the recommended model can align with user demands, providing suitable ventilation performance and air supply parameters. For user-oriented indoor environment construction, this method enables the rapid and accurate determination of recommended air supply parameters and performance, facilitating the creation of a healthy built environment.
Keyword ：
Demand-oriented control Demand-oriented control BP neural network BP neural network Personalized recommendation Personalized recommendation Ventilation method Ventilation method CFD CFD
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Weijia , Zhang, Weirong , Bai, Yifei et al. Enhancing indoor environmental quality: Personalized recommendation method for demand-oriented indoor ventilation strategy [J]. | SUSTAINABLE CITIES AND SOCIETY , 2023 , 101 . |
| MLA | Zhang, Weijia et al. "Enhancing indoor environmental quality: Personalized recommendation method for demand-oriented indoor ventilation strategy" . | SUSTAINABLE CITIES AND SOCIETY 101 (2023) . |
| APA | Zhang, Weijia , Zhang, Weirong , Bai, Yifei , Wen, Shuqing . Enhancing indoor environmental quality: Personalized recommendation method for demand-oriented indoor ventilation strategy . | SUSTAINABLE CITIES AND SOCIETY , 2023 , 101 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Multi-energy supply systems for office buildings have been rapidly developed. Although some combinations of distributed energy sources have been applied in office buildings, there is a lack of research on the collaborative matching of office building multi-energy supply systems that integrate random electric vehicles (EVs), stationary battery (STB), photovoltaic (PV), and the power grid (PG). Therefore, in this study, we propose a method for the collaborative matching of supply and demand for multi-energy systems integrated with random EVs, STB, PV, and PG in office buildings to minimize the net operating cost of the system. To this end, we first constructed an objective function, including system operating cost and operating revenue. We then modeled the supply and demand sides of the system. On the supply side, the travel parameters of EVs around the office building were investigated, and the randomness of EVs was quantified using the Monte Carlo algorithm. On this basis, the charging and discharging capacity of EVs was determined. Additionally, an STB charging and discharging model and PV power generation model were established. On the demand side, a prediction model of office building power consumption was constructed using the Random forest (RF) algorithm. Next, the energy supply and demand of office buildings for the following 24 h were matched collaboratively using nonlinear programming. Finally, the proposed collaborative matching method was applied to a real case study. The results showed that the matching method can reduce the net operating cost of the system by up to 92% compared with the Basic scenario.
Keyword ：
Electric vehicles Electric vehicles Collaborative matching method Collaborative matching method Randomness Randomness Multi-energy supply system Multi-energy supply system Photovoltaic Photovoltaic Random forest Random forest
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Bai, Yifei , Zhang, Weirong , Hu, Xu . A collaborative matching method for multi-energy supply systems in office buildings considering the random characteristics of electric vehicles [J]. | ENERGY AND BUILDINGS , 2023 , 303 . |
| MLA | Bai, Yifei et al. "A collaborative matching method for multi-energy supply systems in office buildings considering the random characteristics of electric vehicles" . | ENERGY AND BUILDINGS 303 (2023) . |
| APA | Bai, Yifei , Zhang, Weirong , Hu, Xu . A collaborative matching method for multi-energy supply systems in office buildings considering the random characteristics of electric vehicles . | ENERGY AND BUILDINGS , 2023 , 303 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
As infectious respiratory diseases are highly transmissible through the air, researchers have improved traditional total volume air distribution systems to reduce infection risk. Multi-vent module-based adaptive ventilation (MAV) is a novel ventilation type that facilitates the switching of inlets and outlets to suit different indoor scenarios without changing ductwork layout. However, little research has evaluated MAV module sizing and air velocity selection, both related to MAV system efficiency in removing contaminants and the corresponding level of protection for occupants in the ventilated room. Therefore, the module-source offset ratio (MSOR) is proposed, based on the MAV module size and its distance from an infected occupant, to inform selection of optimal MAV module parameters. Computational fluid dynamics simulations illustrated contaminant distribution in a two-person MAV equipped office. Discrete phase particles modelled respiratory contaminants from the infected occupant, and contaminant concentration distributions were compared under four MAV air distribution layouts, three air velocities, and three module sizes considered using the MSOR. Results indicate that lower air velocities favour rising contaminant levels, provided the ventilation rate is met. Optimal contaminant discharge can be achieved when the line of outlets is located directly above the infected occupant. Using this parameter to guide MAV system design, 85.7% of contaminants may be rendered harmless to the human body within 120 s using the default air vent layout. A more appropriate supply air velocity and air vent layout increases this value to 91.4%. These results are expected to inform the deployment of MAV systems to reduce airborne infection risk.
Keyword ：
computational fluid dynamics computational fluid dynamics infection control infection control indoor air quality indoor air quality multi-vent module-based adaptive ventilation multi-vent module-based adaptive ventilation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Haotian , Ding, Xiaoxiao , Zhang, Weirong et al. Optimising multi-vent module-based adaptive ventilation using a novel parameter for improved indoor air quality and health protection [J]. | BUILDING SIMULATION , 2023 , 17 (1) : 113-130 . |
| MLA | Zhang, Haotian et al. "Optimising multi-vent module-based adaptive ventilation using a novel parameter for improved indoor air quality and health protection" . | BUILDING SIMULATION 17 . 1 (2023) : 113-130 . |
| APA | Zhang, Haotian , Ding, Xiaoxiao , Zhang, Weirong , Zhang, Weijia , Xuan, Yingli . Optimising multi-vent module-based adaptive ventilation using a novel parameter for improved indoor air quality and health protection . | BUILDING SIMULATION , 2023 , 17 (1) , 113-130 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Cities comprise many residential districts, facing environment and energy problems. Therefore, the retrofit of these existing residential districts is the main part of city renewal. Some retrofit strategies may affect the thermal microclimate environment and lead to changes in the energy demand of buildings. Few studies had evaluated these retrofit effects, which has brought the motivation for this study, to determine the microclimate changes surrounding buildings due to residential district retrofits and the corresponding impact on the cooling load of buildings. The most general retrofit strategies were selected, including greening rate, ground pavement materials, wall materials and roof materials. They were applied to a prediction model for the selected residential district in the simulation tool. The mean reduction of air temperature around the building has been used to evaluate the impact of retrofit strategies on the microclimate in the district. The results showed that greening had the greatest impact on the mean air temperature around buildings, followed by the pavement material, the roof and the wall material. Ground greening, wall material and roof material decreased the total cooling load, while pavement material increased it.
Keyword ：
Retrofit Retrofit Residential district Residential district ENVI-met ENVI-met Strategies Strategies Energy demand Energy demand
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Ruixu , Zhang, Weirong , Qiu, Luo et al. A numerical study on changes in air temperature around buildings due to retrofits in existing residential districts [J]. | INDOOR AND BUILT ENVIRONMENT , 2022 , 31 (6) : 1464-1481 . |
| MLA | Li, Ruixu et al. "A numerical study on changes in air temperature around buildings due to retrofits in existing residential districts" . | INDOOR AND BUILT ENVIRONMENT 31 . 6 (2022) : 1464-1481 . |
| APA | Li, Ruixu , Zhang, Weirong , Qiu, Luo , Zhang, Huibo . A numerical study on changes in air temperature around buildings due to retrofits in existing residential districts . | INDOOR AND BUILT ENVIRONMENT , 2022 , 31 (6) , 1464-1481 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Infectious respiratory diseases are known to have high levels of airborne transmissibility. However, traditional ventilation methods based on perfect mixing often lead to the diffusion of airborne pathogens. A ventilation method that can reduce air mixing is necessary. The ventilation system should also be able to adapt to changes in the use scenario of a given room. For this reason, a new type of ventilation method, referred to as multi-vent module-based adaptive ventilation (MAV), is proposed, and its performance in contaminant diffusion control is evaluated in this study. Computational fluid dynamics (CFD) is applied to investigate the contaminant distribution in a two-desk office with MAV. Tracer gas (CO2) is used to simulate coughed contaminants from an infected person. For three different MAV air distributions (vertical, parallel, and cross mode) and traditional mixing ventilation (MV), the contaminant concentration distributions and contaminant variations in the oronasal areas of the occupants are compared. The results show that MAV results in a smaller contaminant diffusion area and lower contaminant diffusion speed. Furthermore, MAV can reduce the peak contaminant concentrations at the oronasal areas of the occupants to 12% of that of MV. The levels of performance of the three MAV modes are different because of the different airflow patterns that they create. For the tested location of the infected person, the vertical mode has the best performance. The average cumulative inhalation of contaminants in the vertical mode is 23.7% lower than that in the cross mode and 36.5% lower than that in the parallel mode.
Keyword ：
Computational fluid dynamics (CFD) Computational fluid dynamics (CFD) Indoor contaminants Indoor contaminants Cross-infection Cross-infection Probable cumulative inhalation Probable cumulative inhalation Multi-vent module-based adaptive ventilation (MAV) Multi-vent module-based adaptive ventilation (MAV)
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Haotian , Zhang, Weirong , Zhang, Weijia et al. Multi-vent module-based adaptive ventilation to reduce cross-contamination among indoor occupants [J]. | BUILDING AND ENVIRONMENT , 2022 , 212 . |
| MLA | Zhang, Haotian et al. "Multi-vent module-based adaptive ventilation to reduce cross-contamination among indoor occupants" . | BUILDING AND ENVIRONMENT 212 (2022) . |
| APA | Zhang, Haotian , Zhang, Weirong , Zhang, Weijia , Xuan, Yingli , Yue, Yaqi . Multi-vent module-based adaptive ventilation to reduce cross-contamination among indoor occupants . | BUILDING AND ENVIRONMENT , 2022 , 212 . |
| Export to | NoteExpress RIS BibTex |
Export
| Results： |
Selected to |
| Format： |
