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

BRE, FACUNDO; GIMENEZ, JUAN M.

Building Simulation

Springer Nature

Año: 2022

1996-3599

Natural ventilation (NV) is a key passive strategy to design energy-efficient buildings and improveindoor air quality. Therefore, accurate modeling of the NV effects is a basic requirement to includethis technique during the building design process. However, there is an important lack of windpressure coefficients (Cp) data, essential input parameters for NV models. Besides this, there are nosimple but still reliable tools to predict Cp data on buildings with arbitrary shapes and surroundingconditions, which means a significant limitation to NV modeling in real applications. For thisreason, the present contribution proposes a novel cloud-based platform to predict wind pressurecoefficients on buildings. The platform comprises a set of tools for performing fully unattendedcomputational fluid dynamics (CFD) simulations of the atmospheric boundary layer and gettingreliable Cp data for actual scenarios. CFD-expert decisions throughout the entire workflow areimplemented 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 theplatform, an exhaustive validation against wind tunnel experimental data is carried out for a widerange of case studies. These include buildings with openings, balconies, irregular floor-plans, andsurrounding 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. Theplatform introduced shows being a reliable and practical Cp data source for NV modeling in realbuilding design scenarios.