Fig. 5. Setup and results of a fluid-structure optimization problem with 40 × 16 design variables.

a Problem setup: minimizing pressure drop through the tunnel. b Dimensionless inlet pressure versus n_train, the number of accumulated training samples. SOLO-G denotes a greedy version of our proposed method, where the cross “X” denotes the convergence point (presented in d). The horizontal dashed line denotes the solution from the gradient-based method. c Optimized design obtained by the gradient-based method. $\tilde{P}=0.8065$. d Optimized design obtained by SOLO-G. n_train = 1912 and $\tilde{P}=0.8062$. In c, d black denotes ρ = 1 (solid) and white denotes ρ = 0 (void). The SOLO-G result in d has two gaps at the 7th and 12th columns, while the gradient-based result in c gives a smooth ramp. We try filling the gaps and find that their existence indeed reduces pressure, which demonstrates the powerfulness of our method.