Building energy modeling software generally comes with capable airflow network solvers for natural ventilation evaluation in multi-zone building energy models. These approaches rely on arrays of pressure coefficients ...Building energy modeling software generally comes with capable airflow network solvers for natural ventilation evaluation in multi-zone building energy models. These approaches rely on arrays of pressure coefficients representing different wind directions derived from simple box-shaped buildings without contextual obstructions. For urban or obstructed sites, or more complex building shapes, however, further evaluation is needed to avoid geometric oversimplification. In this study, we present an automated and easy-to-use simulation workflow for OpenFOAM-based exterior airflow simulations to generate arrays of pressure coefficients for arbitrary building shapes and contextual situations. The workflow is compared to other methods commonly used to obtain pressure coefficients for natural ventilation analysis. Finally, we assess for which climate zones and building types modelers should rely on more accurate CFD-based pressure coefficients and where it may be justifiable to rely on easier and readily available analytical approaches to determine pressure coefficients. Results suggest that existing workflows lead to significant error in predicted comfort hours for climates in the Global South and modelers should consider CFD-based facade pressure coeficients.展开更多
Natural ventilation(NV)is a key passive strategy to design energy-efficient buildings and improve indoor air quality.Therefore,accurate modeling of the NV effects is a basic requirement to include this technique durin...Natural ventilation(NV)is a key passive strategy to design energy-efficient buildings and improve indoor air quality.Therefore,accurate modeling of the NV effects is a basic requirement to include this technique during the building design process.However,there is an important lack of wind pressure coefficients(CP)data,essential input parameters for NV models.Besides this,there are no simple but still reliable tools to predict CP data on buildings with arbitrary shapes and surrounding conditions,which means a significant limitation to NV modeling in real applications.For this reason,the present contribution proposes a novel cloud-based platform to predict wind pressure coefficients on buildings.The platform comprises a set of tools for performing fully unattended computational fluid dynamics(CFD)simulations of the atmospheric boundary layer and getting reliable CP data for actual scenarios.CFD-expert decisions throughout the entire workflow are implemented to automatize the generation of the computational domain,the meshing procedure the solution stage,and the post-processing of the results.To evaluate the performance of the platform,an exhaustive validation against wind tunnel experimental data is carried out for a wide range of case studies.These include buildings with openings,balconies,irregular floor-plans,and surrounding urban environments.The C_(P) results are in close agreement with experimental data,reducing 60%-77% the prediction error on the openings regarding the EnergyPlus software.The platform introduced shows being a reliable and practical C_(P) data source for NV modeling in real building design scenarios.展开更多
文摘Building energy modeling software generally comes with capable airflow network solvers for natural ventilation evaluation in multi-zone building energy models. These approaches rely on arrays of pressure coefficients representing different wind directions derived from simple box-shaped buildings without contextual obstructions. For urban or obstructed sites, or more complex building shapes, however, further evaluation is needed to avoid geometric oversimplification. In this study, we present an automated and easy-to-use simulation workflow for OpenFOAM-based exterior airflow simulations to generate arrays of pressure coefficients for arbitrary building shapes and contextual situations. The workflow is compared to other methods commonly used to obtain pressure coefficients for natural ventilation analysis. Finally, we assess for which climate zones and building types modelers should rely on more accurate CFD-based pressure coefficients and where it may be justifiable to rely on easier and readily available analytical approaches to determine pressure coefficients. Results suggest that existing workflows lead to significant error in predicted comfort hours for climates in the Global South and modelers should consider CFD-based facade pressure coeficients.
基金For funding this work,we would like to thank the Agencia Nacional de Promocion de la Investigacion,el Desarrollo Tecnologico y la Innovacion(Agencia I+D+i),Argentina,via the projects PICT-2018 N°03252 and PICT-2018 N°02464,Res.N°401-19.
文摘Natural ventilation(NV)is a key passive strategy to design energy-efficient buildings and improve indoor air quality.Therefore,accurate modeling of the NV effects is a basic requirement to include this technique during the building design process.However,there is an important lack of wind pressure coefficients(CP)data,essential input parameters for NV models.Besides this,there are no simple but still reliable tools to predict CP data on buildings with arbitrary shapes and surrounding conditions,which means a significant limitation to NV modeling in real applications.For this reason,the present contribution proposes a novel cloud-based platform to predict wind pressure coefficients on buildings.The platform comprises a set of tools for performing fully unattended computational fluid dynamics(CFD)simulations of the atmospheric boundary layer and getting reliable CP data for actual scenarios.CFD-expert decisions throughout the entire workflow are implemented to automatize the generation of the computational domain,the meshing procedure the solution stage,and the post-processing of the results.To evaluate the performance of the platform,an exhaustive validation against wind tunnel experimental data is carried out for a wide range of case studies.These include buildings with openings,balconies,irregular floor-plans,and surrounding urban environments.The C_(P) results are in close agreement with experimental data,reducing 60%-77% the prediction error on the openings regarding the EnergyPlus software.The platform introduced shows being a reliable and practical C_(P) data source for NV modeling in real building design scenarios.
