Double-skin façades (DSFs) are high-efficiency systems traditionally used to improve the natural ventilation of buildings for energy-saving purposes and their aesthetic improvement. There are opportunities to enhance their functionality by applying them to improve building aerodynamics. This study aims at developing the preliminar data set that would assist with design of smart morphing facades (also known as Smorphacades). For this purpose the a DSF for rectangular and elliptical tall buildings is studied with the final goal of minimizing the aerodynamic loads on such structures. For this purpose, an integrated framework including numerical modeling and statistical analysis was developed. The design of experiment (DOE) method was applied to produce a sufficient dataset based on computational fluid dynamics (CFD) simulation of two-dimensional (2D) scaled models that were validated with available wind tunnel data. According to the data points recommended by the DOE method, a considerable number of CFD simulations were performed, and the drag coefficients of the building and double-skin façade were separately calculated. A prediction model based on the response surface methodology (RSM) was developed to estimate the drag coefficient for other cases inside the design space. Based on the fitted RSM, the Genetic algorithm was applied to search for the optimized Smorphacade shapes. The results indicated that the integrated smorphacade system could significantly mitigate wind-induced drag forces on the building at all attack angles by modifying wind-induced pressure around the building and weakening vortex shedding by interrupting flow separation and ejecting airflow into a lower-pressure area. This research proves that there are opportunitie to integarte the architectural and energy applications of smart double skin facades with wind-reducing effects as a promising solution for overcoming existing challenges for controlling wind-induced load and response of tall buildings.

双层外墙（DSF）是传统上用于改善建筑物自然通风以达到节能目的和改善美观的高效系统。有机会通过应用它们来改善建筑空气动力学来增强其功能。这项研究旨在开发初步数据集，以帮助设计智能变形立面（也称为Smorphacades）。为此，对矩形和椭圆形高层建筑的DSF进行了研究，其最终目标是最大程度地降低此类结构上的空气动力学负荷。为此目的，开发了包括数值建模和统计分析的综合框架。实验设计（DOE）方法用于基于二维（2D）缩放模型的计算流体动力学（CFD）仿真，并利用可用的风洞数据进行验证，从而生成足够的数据集。根据DOE方法推荐的数据点，进行了大量的CFD模拟，并分别计算了建筑物和双层外立面的阻力系数。开发了基于响应面方法（RSM）的预测模型，以估计设计空间内其他情况的阻力系数。基于拟合的RSM，应用遗传算法搜索优化的Smorphacade形状。结果表明，通过改变建筑物周围的风致压力并通过中断流分离和将气流喷射到低压区域来减弱涡旋脱落，集成的闸门系统可以在所有迎角上显着减轻建筑物上的风致阻力。这项研究证明，有机会整合具有减风效果的智能双层蒙皮外墙的建筑和能源应用，作为克服现有挑战以控制高层建筑的风荷载和响应的有前途的解决方案。