A holistic framework to utilize natural ventilation to optimize energy performance of residential high-rise buildings

A novel holistic framework was established using Building Information Modelling (BIM) to estimate accurately the potential of natural ventilation of residential high-rise buildings. This framework integrates Computational Fluid Dynamics (CFD) simulation, multi-zone-air-flow modelling, and Building Energy Simulation (BES) to calculate ventilation rates under the mechanisms of wind-, buoyancy- and wind and buoyancy-driven ventilation. The framework was applied to a 40-storey residential building in Hong Kong for estimating the potential of natural ventilation in residential high-rise buildings. The results show that the building can save up to 25% of the electricity consumption if the building employs wind-driven natural ventilation instead of mechanical ventilation. The electricity consumption can be further reduced up to 45% by facilitating the buoyancy-driven natural ventilation. However, natural ventilation is found to be effective only if the temperature difference between indoor and outdoor is less than 2 °C. The study suggests to orienting residential high-rise buildings at an oblique angle with the prevalent wind direction than positioning perpendicular to the prevalent wind direction. Furthermore, the framework recommends promoting the wind-driven natural ventilation at top floors of residential high-rise buildings and to facilitate wind and buoyancy-driven natural ventilation at middle and lower floors of the buildings.
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Physics-based, data-driven approach for predicting natural ventilation of residential high-rise buildings

Natural ventilation is particularly important for residential high-rise buildings as it maintains indoor human comfort without incurring the energy demands that air-conditioning does. To improve a building’s natural ventilation, it is essential to develop models to understand the relationship between wind flow characteristics and the building’s design. Significantly more effort is still needed for developing such reliable, accurate, and computationally economical models instead of currently the most popular physics-based models such as computational fluid dynamics (CFD) simulation. This paper, therefore, presents a novel model developed based on physics-based modelling and a data-driven approach to evaluate natural ventilation in residential high-rise buildings. The model first uses CFD to simulate wind pressures on the exterior surfaces of a high-rise building. Once the surface pressures have been obtained, multizone modelling is used to predict the air change per hour (ACH) for different flats in various configurations. Data-driven prediction models are then developed using data from the simulation and deep neural networks that are based on mean absolute error, mean absolute percentage error, and a fusion algorithm respectively. These data-driven models are used to predict the ACH of 25 flats. The results from multizone modelling and data-driven modelling are compared. The results imply a high accuracy of the data-driven prediction in comparison with physics-based models. The fusion algorithm-based neural network performs best, achieving 96% accuracy, which is the highest of all models tested. This study contributes a more efficient and robust method for predicting wind-induced natural ventilation. The findings describe the relationship between building design (e.g., plan layout), distribution of surface pressure, and the resulting ACH, which serve to improve the practical design of sustainable buildings.
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CFD simulation of natural ventilation of a generic building in various incident wind directions: Comparison of turbulence modelling, evaluation methods, and ventilation mechanisms

Single-sided and cross- ventilation of a cube-shaped building at various incident wind directions were stimulated using computational fluid dynamics (CFD). The simulation used either the Reynolds-averaged Navier-Stokes (RANS) equations in conjunction with a 2-equation turbulence model (Standard k-e model (SKT), Realizable k-e model (RLZ), or Renormalization group k-e model (RNG)), or Large Eddy Simulation (LES) with the Wall-Adapting Local Eddy-viscosity model (WALE). LES showed good agreement with wind tunnel data when modeling indoor and outdoor wind fields; RLZ had slightly better results than SKT and RNG. Two evaluation methods were examined: the integration of opening velocities method under-estimated single-sided ventilation rates but over-estimated cross-ventilation rates; the tracer-gas decay method was more computationally demanding of the two. When the tracer-gas method and LES were used in combination, their estimations were most accurate especially if the ventilation was driven by wind flow fluctuations. RANS and LES predicted steady decreases of 92.5% and 81.8% (single-sided ventilation), and 52.6% and 37.2% (cross-ventilation) in ventilation rate as incident wind direction varies from 0
to 90
. Discrepancies between the RANS and LES predictions of ventilation rates were mainly attributable to their respective turbulence-modeling methods.
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Cross-ventilation of a generic building with various configurations of external and internal openings

Cross-ventilation is the most effective mode of wind-driven natural ventilation but completely depends on the external openings and internal layout. This study investigates how openings affect cross-ventilation of a generic four-wall space using Computational Fluid Dynamics (CFD) simulation. The building has one to four external openings of two different sizes, and the interior contains various configurations of vertical and horizontal walls with blockage ratios varying between 5% and 20%. Two-opening configurations proved to be tremendously efficient for cross-ventilation; for all similarly sized openings, the two-opening scenario doubles the ventilation rate of the single-opening scenario. Using a larger opening on the windward or leeward surface, the two-opening configuration achieved higher ventilation rates and better indoor wind circulation than both the three- and four opening configurations. In turn, to enhance cross-ventilation in three- and four-opening configurations, more than one of the openings needs to be larger than the rest. The presence of internal walls always led to smaller ventilation rates than no internal walls; such reduction steadily increased with internal blockage ratio. The reduction in ventilation rates can be minimized by adopting several narrow internal walls instead of one wide vertical wall and positioning horizontally-oriented walls above the opening height.
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