Natural ventilation for office building retrofit in dense urban context under hot and humid climate

Recent studies underline that simple and non-invasive retrofit solutions can recover natural ventilation potential in existing buildings under temperate climate. Nonetheless, the efficiency of these solutions in dense urban contexts under hot and humid climate remains unclear. This paper aims to evaluate the thermal comfort gains caused by natural ventilation when retrofitting an office building in downtown Rio de Janeiro. Computational Fluid Dynamics (CFD) and thermal simulations are carried out on Ansys CFX and Design builder to assess indoor air flow before and after retrofit. The diagnosis of the current scenario indicates that the surrounding buildings block a significant part of the wind flow, and occupants experience only a few hours of thermal comfort during the year, especially on lower floors. To increase indoor air flow, the fixed upper windows were transformed into pivot windows and kept open permanently. This measure increases the annual hours of thermal comfort by 0.5-35%, depending on the floor and the adaptive comfort model. These findings suggest that natural ventilation itself may not be sufficient to ensure occupants' comfort throughout the year under the investigated context.

Modeling of Indoor Air Flow Distribution in a Naturally Ventilated Kitchen

This paper focuses on the modeling of indoor air pollution in a naturally ventilated kitchen based on the computational fluid dynamics (CFD) approach to assess its ventilation effectiveness. The 3D incompressible Navier-Stokes equations with conservation of total energy are solved numerically using ANSYS-Fluent software and the pollutant paths are investigated from the profiles of velocity, pressure, turbulent kinetic energy and temperature throughout different sections of the kitchen. Experimental verification is made through the measurement of indoor air contaminant in the same kitchen. The simulation results agree well with the on-site measured data.