Fig. 5: Setup and results of a fluid-structure optimization problem with 40 × 16 design variables.
From: Self-directed online machine learning for topology optimization
a Problem setup: minimizing pressure drop through the tunnel. b Dimensionless inlet pressure versus ntrain, 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. \(\widetilde{P}=0.8065\). d Optimized design obtained by SOLO-G. ntrain = 1912 and \(\widetilde{P}=0.8062\). 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.
