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

Autor: Bre F; Centro de Investigación de Métodos Computacionales (CIMEC), UNL, CONICET, Predio 'Dr. Alberto Cassano', Colectora Ruta Nacional 168 s/n, 3000 Santa Fe, Argentina., Gimenez JM; Centro de Investigación de Métodos Computacionales (CIMEC), UNL, CONICET, Predio 'Dr. Alberto Cassano', Colectora Ruta Nacional 168 s/n, 3000 Santa Fe, Argentina.
Jazyk: angličtina
Zdroj: Building simulation [Build Simul] 2022; Vol. 15 (8), pp. 1507-1525. Date of Electronic Publication: 2022 Jan 22.
DOI: 10.1007/s12273-021-0881-9
Abstrakt: 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 ( C p ) data, essential input parameters for NV models. Besides this, there are no simple but still reliable tools to predict C p 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 C p 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.
Electronic Supplementary Material Esm: The appendix is available in the online version of this article at 10.1007/s12273-021-0881-9.
(© Tsinghua University Press 2022.)
Databáze: MEDLINE
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