  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 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.
