Aerodynamic optimization of twisted tall buildings

Tall buildings are distinguished by their slenderness, making them sensitive to wind loads. A huge amount of resources is typically dedicated to controlling loads and vibrations caused by wind. Enhancing tall buildings' aerodynamic performance can save a large portion of these expenses. This enhancement can be achieved through aerodynamic optimization that can be tackled either by altering the outer shape of the building locally through modifying the corners (e.g., corner chamfering) or globally through changing the whole form of the building (e.g., twisting). In this paper, a newly developed aerodynamic optimization procedure (AOP) is adopted to enhance tall buildings' aerodynamic performance. This procedure is a combination of computational fluid dynamics (CFD), Artificial Neural Networks (ANN) and Genetic algorithm (GA). An ANNbased surrogate model is used to evaluate the aerodynamic parameters through the optimization procedure to reach a reliable aerodynamic shape. Helical twisting and corner modifications of the buildings are used to reduce the along-wind base moment.