Reducing the intensity of wind excitation via aerodynamic shape modification is a major strategy to mitigate the reaction forces on supertall buildings, reduce construction and maintenance costs, and improve the comfort of future occupants. To this end, computational fluid dynamics (CFD) combined with state-of-the-art stochastic optimization algorithms is more promising than the trial and error approach adopted by the industry. The present study proposes and investigates a novel approach to risk-averse shape optimization of tall building structures that incorporates site-specific uncertainties in the wind velocity, terrain conditions, and wind flow direction. A body-fitted finite element approximation is used for the CFD with different wind directions incorporated by re-meshing the fluid domain. The bending moment at the base of the building is minimized, resulting in a building with reduced cost, material, and hence, a reduced carbon footprint. Both risk-neutral (mean value) and risk-averse optimization of the twist and tapering of a representative building are presented under uncertain inflow wind conditions that have been calibrated to fit freely-available site-specific data from Basel, Switzerland. The risk-averse strategy uses the conditional value-at-risk to optimize for the low-probability high-consequence events appearing in the worst 10% of loading conditions. Adaptive sampling is used to accelerate the gradient-based stochastic optimization pipeline. The adaptive method is easy to implement and particularly helpful for compute-intensive simulations because the number of gradient samples grows only as the optimal design algorithm converges. The performance of the final risk-averse building geometry is exceptionally favorable when compared to the risk-neutral optimized geometry, thus, demonstrating the effectiveness of the risk-averse design approach in computational wind engineering.

通过气动形状修改降低风激励强度是减轻超高层建筑的反作用力、降低建设和维护成本、提高未来居住者舒适度的主要策略。为此，计算流体动力学 (CFD) 与最先进的随机优化算法相结合，比业界采用的试错法更有前景。本研究提出并研究了一种新的方法来优化高层建筑结构的风险规避形状，该方法结合了风速、地形条件和风流方向中特定地点的不确定性。贴体有限元近似用于通过重新划分流体域的网格来合并不同风向的 CFD。建筑物底部的弯矩被最小化，从而使建筑物的成本和材料降低，从而减少了碳足迹。在不确定的流入风条件下呈现了代表性建筑物的扭曲和锥形的风险中性（平均值）和风险规避优化，这些条件已经过校准以适合瑞士巴塞尔免费提供的特定地点数据。风险规避策略使用条件风险价值来优化在最坏的 10% 负载条件下出现的低概率高后果事件。自适应采样用于加速基于梯度的随机优化管道。自适应方法易于实现，并且对于计算密集型模拟特别有用，因为梯度样本的数量仅随着最优设计算法的收敛而增长。与风险中性优化的几何结构相比，最终规避风险的建筑几何结构的性能异常有利，因此证明了风险规避设计方法在计算风能工程中的有效性。