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.

Pedestrian wind comfort near a 400 m super-tall building in high and low ambient wind speeds, referred to as Windy and Calm climates, is evaluated by conducting computational fluid dynamics (CFD) simulations. The super-tall building has 15 different configurations and is located at the center of 50 m medium-rise buildings in an urban-like setting. Pedestrian level mean wind speeds near the super-tall building is obtained from three-dimensional (3D), steady-state, Reynolds-Averaged Navier-Stokes (RANS)-based simulations for five incident wind directions (θ = 0°, 22.5°, 45°, 90°, 180°) that are subsequently compared with two wind comfort criteria specified for Calm and Windy climates. Results show a 1.53 times increase in maximum mean wind speed in the urban area after the construction of a square-shaped super-tall building. The escalated mean wind speeds result in a 23%-15% and 36%-29% decrease in the area with "acceptable wind comfort" in Calm and Windy climates, respectively. The area with pedestrian wind comfort varies significantly with building configuration and incident wind direction, for example, the configurations with sharp corners, large plan aspect ratios and, frontal areas and the orientation consistently show a strong dependency on incident wind direction except for the one with rounded plan shapes. Minor aerodynamic modifications such as corner modifications and aerodynamically-shaped configurations such as tapered and setback buildings show promise in improving pedestrian wind comfort in Windy climate.