Using large eddy simulation to model buoyancy-driven natural ventilation

The use of Large Eddy Simulation (LES) for modelling air flows in buildings is a growing area of Computational Fluid Dynamics (CFD). Compared to traditional CFD techniques, LES provides a more detailed approach to modelling turbulence in air. This offers the potential for more accurate modelling of low energy natural ventilation which is notoriously difficult to model using traditional CFD. Currently, very little is known about the performance of LES for modelling natural ventilation, and its computational intensity makes its practical use on desk top computers prohibitive. The objective of this work was to apply LES to a variety of natural ventilation strategies and to compile guidelines for practitioners on its performance, including the trade-off between accuracy and cost

Adaptive comfort model incorporating temperature gradient for a UK residential building

Thermal comfort field experiments were conducted to acquire thermal comfort data of 119 participants in a test house representative of a typical UK house. This paper compares the performance of popular PMV-based thermal comfort index vs neutral temperature based on Actual Mean Vote. The aim of this research was to incorporate vertical thermal gradient, which is usually a neglected yet highly influential parameter in a residential setting and propose a new adaptive thermal comfort model. The new adaptive model (LPMV) has been developed using a polynomial curve fit method. This method was chosen as it has the capability to correlate indoor environmental parameters with AMV and incorporated them in the generated mathematical model. The model requires temperature gradient and SET* only to determine neutral temperatures which makes it the first of its kind. The LMPV model was rigorously tested against thermal comfort data compiled in this study and against independent/unbiased data (the ASHRAE RP-884 database). LPMV showed up to 0.7°C improvement in predicting neutral temperature of occupants compared to the famous Fanger’s PMV model. This can result in better prediction of a suitable heating setpoint temperature which has great implications on annual energy demand