Infectious disease departments in hospitals require pressure gradient to create unidirectional airflow to prevent the spread of contaminants,typically by creating active air infiltration through the difference between...Infectious disease departments in hospitals require pressure gradient to create unidirectional airflow to prevent the spread of contaminants,typically by creating active air infiltration through the difference between supply and exhaust air volumes.The door gap is the channel of air flow between rooms,so its height has an important influence on the pressure difference and infiltration air volume of the room.There is still a lack of research on setting reasonable ventilation strategies according to the different heights of door gaps at different positions in the building.In this study,model of a set of isolation wards was established and analyzed using the multi-zone simulation software CONTAM,and the ventilation strategies with different heights of door gaps were applied to the actual infection diseases department.The results show that in a building with ventilation system divided by functional area,the difference in the height of the door gaps requires different active infiltration air volumes.Pressure fluctuations in the medical and patient corridors are greater than in other rooms.The significance of this study is to understand the active infiltration of air to guide the design and operation of ventilation systems in infectious disease hospitals or building remodeled to isolate close contacts of COVID-19 patients.It is also instructive for the design of pressure gradients in clean workshops,biological laboratories,and other similar buildings.展开更多
Natural ventilation is particularly important for residential high-rise buildings as it maintains indoor human comfort without incurring the energy demands that air-conditioning does.To improve a building’s natural v...Natural ventilation is particularly important for residential high-rise buildings as it maintains indoor human comfort without incurring the energy demands that air-conditioning does.To improve a building’s natural ventilation,it is essential to develop models to understand the relationship between wind flow characteristics and the building's design.Significantly more effort is still needed for developing such reliable,accurate,and computationally economical models instead of currently the most popular physics-based models such as computational fluid dynamics(CFD)simulation.This paper,therefore,presents a novel model developed based on physics-based modelling and a data-driven approach to evaluate natural ventilation in residential high-rise buildings.The model first uses CFD to simulate wind pressures on the exterior surfaces of a high-rise building.Once the surface pressures have been obtained,multizone modelling is used to predict the air change per hour(ACH)for different flats in various configurations.Data-driven prediction models are then developed using data from the simulation and deep neural networks that are based on mean absolute error,mean absolute percentage error,and a fusion algorithm respectively.These data-driven models are used to predict the ACH of 25 flats.The results from multizone modelling and data-driven modelling are compared.The results imply a high accuracy of the data-driven prediction in comparison with physics-based models.The fusion algorithm-based neural network performs best,achieving 96%accuracy,which is the highest of all models tested.This study contributes a more efficient and robust method for predicting wind-induced natural ventilation.The findings describe the relationship between building design(e.g.,plan layout),distribution of surface pressure,and the resulting ACH,which serve to improve the practical design of sustainable buildings.展开更多
文摘Infectious disease departments in hospitals require pressure gradient to create unidirectional airflow to prevent the spread of contaminants,typically by creating active air infiltration through the difference between supply and exhaust air volumes.The door gap is the channel of air flow between rooms,so its height has an important influence on the pressure difference and infiltration air volume of the room.There is still a lack of research on setting reasonable ventilation strategies according to the different heights of door gaps at different positions in the building.In this study,model of a set of isolation wards was established and analyzed using the multi-zone simulation software CONTAM,and the ventilation strategies with different heights of door gaps were applied to the actual infection diseases department.The results show that in a building with ventilation system divided by functional area,the difference in the height of the door gaps requires different active infiltration air volumes.Pressure fluctuations in the medical and patient corridors are greater than in other rooms.The significance of this study is to understand the active infiltration of air to guide the design and operation of ventilation systems in infectious disease hospitals or building remodeled to isolate close contacts of COVID-19 patients.It is also instructive for the design of pressure gradients in clean workshops,biological laboratories,and other similar buildings.
基金supported by the Hong Kong University of Science and Technology Research Grant(project no.IGN17EG04).
文摘Natural ventilation is particularly important for residential high-rise buildings as it maintains indoor human comfort without incurring the energy demands that air-conditioning does.To improve a building’s natural ventilation,it is essential to develop models to understand the relationship between wind flow characteristics and the building's design.Significantly more effort is still needed for developing such reliable,accurate,and computationally economical models instead of currently the most popular physics-based models such as computational fluid dynamics(CFD)simulation.This paper,therefore,presents a novel model developed based on physics-based modelling and a data-driven approach to evaluate natural ventilation in residential high-rise buildings.The model first uses CFD to simulate wind pressures on the exterior surfaces of a high-rise building.Once the surface pressures have been obtained,multizone modelling is used to predict the air change per hour(ACH)for different flats in various configurations.Data-driven prediction models are then developed using data from the simulation and deep neural networks that are based on mean absolute error,mean absolute percentage error,and a fusion algorithm respectively.These data-driven models are used to predict the ACH of 25 flats.The results from multizone modelling and data-driven modelling are compared.The results imply a high accuracy of the data-driven prediction in comparison with physics-based models.The fusion algorithm-based neural network performs best,achieving 96%accuracy,which is the highest of all models tested.This study contributes a more efficient and robust method for predicting wind-induced natural ventilation.The findings describe the relationship between building design(e.g.,plan layout),distribution of surface pressure,and the resulting ACH,which serve to improve the practical design of sustainable buildings.
