Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Efficient Prediction of Indoor Airflow in Naturally Ventilated Residential Buildings Using a CFD-DNN Model Approach
https://orcid.org/http://orcid.org/0000-0002-5890-0277
Predicting indoor airflow in multi-storey residential buildings is crucial for energy-efficient natural ventilation systems. The indoor environment significantly affects human well-being due to extended indoor time and potential health risks. Precise and efficient airflow simulations are necessary to ensure thermal comfort and air quality. This study introduces a novel approach combining Computational Fluid Dynamics (CFD) simulations with machine learning techniques to predict indoor airflow. Specifically, we explore using a Deep Neural Network (DNN) model for accurate indoor airflow forecasting. The DNN effectively reproduces airflow patterns and temperature distributions. Integrating CFD simulations halves test scenario anticipation time, highlighting efficient indoor airflow prediction potential. Using a data-driven approach, this research demonstrates the feasibility of swiftly and accurately predicting indoor airflow in naturally ventilated residential buildings. Such models can optimize indoor air quality, thermal comfort, and energy efficiency, contributing to sustainable building design and operation.
Efficient Prediction of Indoor Airflow in Naturally Ventilated Residential Buildings Using a CFD-DNN Model Approach
https://orcid.org/http://orcid.org/0000-0002-5890-0277
Predicting indoor airflow in multi-storey residential buildings is crucial for energy-efficient natural ventilation systems. The indoor environment significantly affects human well-being due to extended indoor time and potential health risks. Precise and efficient airflow simulations are necessary to ensure thermal comfort and air quality. This study introduces a novel approach combining Computational Fluid Dynamics (CFD) simulations with machine learning techniques to predict indoor airflow. Specifically, we explore using a Deep Neural Network (DNN) model for accurate indoor airflow forecasting. The DNN effectively reproduces airflow patterns and temperature distributions. Integrating CFD simulations halves test scenario anticipation time, highlighting efficient indoor airflow prediction potential. Using a data-driven approach, this research demonstrates the feasibility of swiftly and accurately predicting indoor airflow in naturally ventilated residential buildings. Such models can optimize indoor air quality, thermal comfort, and energy efficiency, contributing to sustainable building design and operation.
Efficient Prediction of Indoor Airflow in Naturally Ventilated Residential Buildings Using a CFD-DNN Model Approach
https://orcid.org/http://orcid.org/0000-0002-5890-0277
Predicting indoor airflow in multi-storey residential buildings is crucial for energy-efficient natural ventilation systems. The indoor environment significantly affects human well-being due to extended indoor time and potential health risks. Precise and efficient airflow simulations are necessary to ensure thermal comfort and air quality. This study introduces a novel approach combining Computational Fluid Dynamics (CFD) simulations with machine learning techniques to predict indoor airflow. Specifically, we explore using a Deep Neural Network (DNN) model for accurate indoor airflow forecasting. The DNN effectively reproduces airflow patterns and temperature distributions. Integrating CFD simulations halves test scenario anticipation time, highlighting efficient indoor airflow prediction potential. Using a data-driven approach, this research demonstrates the feasibility of swiftly and accurately predicting indoor airflow in naturally ventilated residential buildings. Such models can optimize indoor air quality, thermal comfort, and energy efficiency, contributing to sustainable building design and operation.
Efficient Prediction of Indoor Airflow in Naturally Ventilated Residential Buildings Using a CFD-DNN Model Approach
Lecture Notes in Civil Engineering
Guo, Wei (Herausgeber:in) / Qian, Kai (Herausgeber:in) / Tang, Honggang (Herausgeber:in) / Gong, Lei (Herausgeber:in) / Van Quang, Tran (Autor:in) / Phuong, Nguyen Lu (Autor:in) / Doan, Dat Tien (Autor:in)
International Conference on Green Building, Civil Engineering and Smart City ; 2023 ; Guiyang, China
02.02.2024
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Coupled simulations for naturally ventilated residential buildings
| Online Contents | 2008
Coupled simulations for naturally ventilated residential buildings
| Online Contents | 2008
Coupled simulations for naturally ventilated residential buildings
| British Library Online Contents | 2008