Achieving Thermal Comfort Using Intelligent Windows in Buildings - Pokhrel, Manoj Kumar (Auckland University of Technology, 2021)
In temperate climatic conditions, residents often ventilate their houses naturally by opening windows. Though numerous studies have examined ventilation in buildings, few have attempted to modulate naturally ventilated spaces' thermal behaviour actively using just the windows. Maintaining a house's thermal comfort characteristics by modulating natural ventilation is particularly challenging, as accurately predicting natural ventilation is a complicated task, and the solution is not explicit. This is due to various non-linear, dynamic and unpredictable environmental and operating factors that influence natural ventilation's driving forces (wind and buoyancy). Furthermore, the potential for natural ventilation to regulate thermal behaviour using windows and its influence on convective heat transfer from indoor surfaces is also not explicit. To address this issue, this work utilised a coupled thermal and network airflow to perform a building performance evaluation. In the first instance, the work examined the potential for regulating the thermal behaviour of a single-sided naturally ventilated model house by opening or shutting a window considering the NZ climatic, building and operating conditions. The study identified significant scope for regulating the thermal behaviour by opening windows. The range improved considerably for a relatively airtight and insulated house during the summer period. However, the work realised that such thermal-airflow model’s robustness needs greater scrutiny; particularly the method for determining the indoor surface’s convective heat transfer coefficient. To scrutinise natural ventilation's influence on the heat transfer process, the research considered the case of a single-sided partly open air-filled cubicle enclosure with a heated floor (analogous to a room exposed to solar radiation). In doing this, Computational Fluid Dynamics (CFD) was used to determine a relationship to describe the heat transfer by natural convection from the floor surface. The relationship showed that the heat transfer can be expressed in terms of the Nusselt number (Nu), Rayleigh number (Ra) and window opening factor (WOF) or aspect ratio (d^'/D^' ), and expressed in the form of either Nu=0.1593.〖Ra〗^0.33.〖WOF〗^0.18 or Nu=0.17.〖Ra〗^0.33.(d^'/D^' )^0.18. The work also observed that there was a significant variation in flow fields in 3D space resulting in a non-uniform distribution of floor heat flux on the spatial spectrum. From this, it was apparent that there was a need to understand the added influence of wind conditions. Thus, the research further examined the flow in, and heat transfer from the floor of, the same enclosure developed in the CFD environment while considering the impact of outdoor wind conditions (wind speed and direction). The investigation showed that changes in the wind conditions could significantly change flow regimes and temperature distributions inside the space, leading to a significant variation in the convective heat transfer from the floor. This micro analysis of computational model deduced another relationship to estimate the heat transfer coefficient for the floor 〖(h〗_(c,floor)) of a naturally ventilated building as a function of wind speed at a meteorological height 〖(V〗_m) and direction (∅), and can be expressed as: h_(c,floor)=a+(b.V_m )+(c.∅)+(d.〖V_m〗^2 )+ (e.V_m.∅)+(f.∅^2). Hence, the robustness of the building’s thermal airflow model was improved by including these specifically deduced convective heat transfer relationships to systematically analyse the effect of buoyancy and wind. A dimensionless Archimedes Number (Ar) was used to determine the dominant effect (wind or buoyancy) causing the airflow through the opening and switch the respective heat transfer relationships for the floor. Further analysis of the dynamic simulations reconfirmed the potential of regulating thermal behaviour by actively modulating window openings. Finally, a solution for maintaining a house's thermal comfort characteristics by modulating natural ventilation required a technique that could adjust the opening area while encompassing the complexity, dynamics, and non-linearity associated with the natural ventilation driving forces and the building thermal behaviour. The work addressed this issue by applying an artificial intelligence technique – Artificial Neural Network (ANN), using a co-simulation environment of Transient System Simulation (TRNSYS) and Matrix Laboratory (MATLAB). The ANN-based model, trained using the dynamic simulations database, was used to actuate window intelligently and modulate the natural ventilation to maintain the indoor thermal comfort level during the summer months. The intelligent window actuating model helped ensure the ventilated space's thermal comfort condition for more than 90% instances during the summer period. Furthermore, the research work confirmed that expanding the ANN technique to control additional heating equipment ensured the ventilated space's thermal comfort for more than 96% instances over the year. In summary, the use of the new correlations describing the heat transfer processes in the building and ANN appear to offer a positive outlook in the development of intelligent control of actuated windows for the next generation naturally ventilated sustainable buildings. The study also provides a significant benefit to delivering a better-built environment by achieving better thermal comfort, building energy efficiency and indoor air quality.
Keywords: Natural Ventilation; Thermal Comfort; Intelligent Window; Artificial Neural Network; Residential House