## 5 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Case**} & \multicolumn{8}{c}{**Along-Wind**} & \multicolumn{8}{c}{**Cross-Wind**} \\  & \multicolumn{2}{c}{\(\mathbf{Disp_{x}}\) [m]} & \multicolumn{2}{c}{\(\mathbf{Acc_{x}}\) [m/s\({}^{2}\)]} & \multicolumn{2}{c}{\(\mathbf{Disp_{y}}\) [m]} & \multicolumn{2}{c}{\(\mathbf{Acc_{y}}\) [m/s\({}^{2}\)]} \\  & _Mean_ & _RMS_ & _Max_ & _Mean_ & _RMS_ & _Max_ & _Mean_ & _RMS_ & _Max_ & _Mean_ & _RMS_ & _Max_ \\ \hline
**Baseline** & 0.18 & 0.21 & 0.53 & 0.00 & 0.17 & 0.52 & 0.00 & 0.09 & 0.26 & 0.00 & 0.16 & 0.47 \\
**With TMD** & 0.18 & 0.19 & 0.39 & 0.00 & 0.08 & 0.23 & 0.00 & 0.06 & 0.17 & 0.00 & 0.08 & 0.25 \\ _Diff. to baseline_ & \([\%]\) & _0.0_ & \(-9.5\) & \(-26.4\) & _0.0_ & \(-52.9\) & \(-55.8\) & _0.0_ & \(-33.3\) & \(-34.6\) & _0.0_ & \(-50.0\) & \(-46.8\) \\
**With TSD** & 0.18 & 0.19 & 0.38 & 0.00 & 0.08 & 0.25 & 0.00 & 0.07 & 0.20 & 0.00 & 0.11 & 0.33 \\ _Diff. to baseline_ & \([\%]\) & _0.0_ & \(-9.5\) & \(-28.3\) & _0.0_ & \(-52.9\) & \(-51.9\) & _0.0_ & \(-22.2\) & \(-23.1\) & _0.0_ & \(-31.3\) & \(-29.5\) \\
**Diff. to TMD** & \([\%]\) & _0.0_ & _0.0_ & \(-2.6\)_ & _0.0_ & _0.0_ & _8.7_ & _0.0_ & _16.7_ & _17.6_ & _0.0_ & **37.5** & **32.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of the kinematics of the top point under turbulent wind loading. A TMD was set up using an assumption of the linear mechanical model particularly tuned to the eigenfrequencies of the building. For TSDs, this is the analogous workflow, yet the tuning process is more complicated. It needs additional considerations of the initial representative response and the amount of mitigation that is aimed to be achieved. Thus, the design is a function of the response amplitude. Altering load conditions would theoretically lead to different optima. We based our considerations on a representative amplitude of acceleration of around (0.15-0.2) m/s\({}^{2}\) for the baseline structure. This value seems to be more appropriate for the broad-band load case, and sub-optimal for the narrow-banded one. A proper decision for each particular design will need to be taken, weighing in the relative importance of specific load cases. For the particular scenario of smooth flow, both AMDs achieve a considerable improvement of close to 100% cross-wind. In the along-wind direction, the TSD performs  
Figure 11: Time series of the kinematics of the top point in smooth flow. clearly worse than the TMD. It needs to be mentioned that, for this load case, the harmonic loading around the weak axis is more critical. Still, the relative performance in this direction shows a considerable difference of 100%, in favor of the TMD. Accompanying these qualitative and quantitative outcomes on the direct effect of AMDs on structures in wind, we provide a snapshot of representative results. Using computational methods permits analyzing all systems involved at full scale, at various levels of detail. In Figure 12, we include typical data that help to visualize: wind loading using CFD, the structural response captured with CSD and the sloshing motion enabled by CFD. This depiction complements the abstraction of the workflow shown in Figure 9.  
The complexity of the models and computational effort for the  
In Table 6, computed core hour values are shown, which we documented for simulations carried out on the same hardware. It can be observed that CFD simulations with turbulent wind will require more capacities because there needs to be a refinement of the mesh from the inlet to the structure to maintain approaching turbulence. Additionally, the total simulation time is typically longer. Despite a larger time step (in this particular case), the resulting effort can be two to three times higher than for the CFD simulation in the case of smooth flow. FSI approaches tend to increase the cost by a factor of at least 2 due to TWC. Including AMDs into the workflow further magnifies requirements by at least a factor of (1.5-2).