Fig. 4: Setup and results of a fluid-structure optimization problem with 20 × 8 design variables.
From: Self-directed online machine learning for topology optimization
a Problem setup: minimizing pressure drop through the tunnel. The vertical green line on the left denotes the inlet while the vertical blue line on the right denotes the outlet. b Dimensionless inlet pressure versus ntrain, the number of accumulated training samples. SOLO-G denotes a greedy version of our proposed method, SOLO-R denotes the regular version of our proposed method. The horizontal dashed line denotes the solution from the gradient-based method. The cross “X” denotes the convergence point (presented in d and e, respectively). c Optimized design obtained by the gradient-based method. \(\widetilde{P}=0.9569\). d Optimized design obtained by SOLO-G. ntrain = 286 and \(\widetilde{P}=0.9567\). e Optimized design obtained by SOLO-R. ntrain = 2148 and \(\widetilde{P}=0.9567\). In c–e black denotes ρ = 1 (solid) and white denotes ρ = 0 (void). The solutions in d and e are equivalent since the flow is blocked by the black squares forming the ramp surface and the white squares within the ramp at the left bottom corner are irrelevant.
