A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Physics-based, data-driven approach for predicting natural ventilation of residential high-rise buildings
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.
Physics-based, data-driven approach for predicting natural ventilation of residential high-rise buildings
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.
Physics-based, data-driven approach for predicting natural ventilation of residential high-rise buildings
Build. Simul.
Gan, Vincent J. L. (author) / Wang, Boyu (author) / Chan, C. M. (author) / Weerasuriya, A. U. (author) / Cheng, Jack C. P. (author)
Building Simulation ; 15 ; 129-148
2022-01-01
20 pages
Article (Journal)
Electronic Resource
English
computational fluid dynamics , data-driven prediction , machine learning , multizone model , natural ventilation , residential building Engineering , Building Construction and Design , Engineering Thermodynamics, Heat and Mass Transfer , Atmospheric Protection/Air Quality Control/Air Pollution , Monitoring/Environmental Analysis
Natural ventilation of high-rise high-density residential buildings of Hong Kong
| BASE | 2015
Enhancement of natural ventilation in high-rise residential buildings using stack system
| Online Contents | 2004