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A cloud-based platform to predict wind pressure coefficients on buildings

Abstract

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 Cp 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 Cp data source for NV modeling in real building design scenarios.

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Acknowledgements

For funding this work, we would like to thank the Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación (Agencia I+D+i), Argentina, via the projects PICT-2018 No03252 and PICT-2018 No02464, Res. No401-19.

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Correspondence to Facundo Bre.

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For a closer analysis or its reproduction, the results and the input geometry data used to generate them can be found at https://doi.org/10.5281/zenodo.5796295.

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Bre, F., Gimenez, J.M. A cloud-based platform to predict wind pressure coefficients on buildings. Build. Simul. 15, 1507–1525 (2022). https://doi.org/10.1007/s12273-021-0881-9

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Keywords

  • natural ventilation
  • building simulation
  • airflow network model
  • EnergyPlus
  • wind pressure coefficient
  • computational fluid dynamics