Curvilinear building forms are increasingly common in modern architecture, yet their impact on wind dynamics remains understudied. This research examines the wind flow behavior around high-rise residential building rows configured with convex and concave curvilinear shapes, focusing on the influence of varying central angles. Using parametric models generated in Rhino’s Grasshopper, followed by experimentally validated CFD simulations, we analyze the aerodynamic effects of these configurations. The findings indicate contrasting impacts: convex curvilinear rows amplify wind flow as their central angles increase, while concave rows hinder airflow under similar conditions. These variations are linked to changes in the windward projective areas. This study enhances the understanding of wind behavior in urban settings and provides key insights for optimizing natural ventilation and designing sustainable curvilinear building clusters.

Although T-form buildings have been widely observed in newly constructed high-rise residential communities, there have been relatively limited investigations into the influence of their geometries on wind environments. This study aims to address this gap by conducting iterative quantitative assessments of the influences of various bulge-part sizes of typical T-form high-rise residential buildings on surrounding wind environments. A methodology has been employed by integrating multiple computational tools, including parametric modeling, Computational Fluid Dynamics (CFD), and Influenced Areas (IAs) analysis. Representative T-form buildings have been modeled with parametric components, allowing for easy variation of bulge-part sizes. The investigation process involves sequential steps of parametric modeling, experimentally validated CFD simulations, statistical assessment, and subsequent results analysis and discussions. Findings could be summarized as follows: (1) according to IAs analysis, the influences on wind environments were decreased as the bulge-part sizes were increased, and the decrease of the bulge-part sizes could cause the contrary effect; (2) the promotion of outdoor ventilation caused by the increase of the bulge-part length was more than the increase of the bulge-part width according to the correlation coefficients (0.88 > 0.78; 0.88 > 0.76); (3) it was recommended to design relatively large bulge parts on the windward side to enhance outdoor ventilation. The research outcomes provide valuable and insightful information for the development of sustainable architectural design strategies aimed at optimizing natural ventilation.

Wind environment is important in architectural sustainable design, as existing studies show that it can be considerably influenced by building morphologies. This study aimed to develop a data-mining framework to quantitatively evaluate and compare influences on Low-Wind-Velocity Area (LWVA) of common cuboid-form buildings with typical morphological parameters. The data-mining framework was originally developed by integrating multiple computational methods for rapid in-depth iterative analyses, including the generation of building models using parametric modelling, the big data generation based on hybrid model, and the statistical metric analysis method. The hybrid model was created by combining the CFD model and machine learning model. The accuracy and efficiency of the framework were fully demonstrated through the comprehensive validation and analyses of different models. The data of more than fifty thousand building cases with different morphological parameters and relevant wind conditions were generated and analyzed. Influences on LWVA of morphological parameters of cuboid-form building was comprehensively evaluated, including the visualization of multiple parameters, calculation and comparison of several correlation coefficients. It suggested that the reduction of height and width on the windward side would significantly decrease the LWVA and promote the outdoor ventilation. The change of depth would have relatively limited influence on the LWVA. Multivariate regression model-fit and variance analyses were further implemented, and it was found that there was a relatively significant linear correlation between the LWVA and morphological parameters. The equation of multivariate regression model was provided for extra rapid prediction. The study outcome could contribute to efficient evaluation of LWVA and provide useful information for sustainable design.