Abstract
Optimal arrangements of turbulent pipe systems strongly depend on branch patterns, and turbulence fields typically cause involved multimodality in the solution space. These features hinder gradient-based structural optimization frameworks from finding promising solutions for turbulent pipe systems. In this paper, we propose a multi-stage framework that integrates data-driven morphological exploration and evolutionary shape optimization to address the challenges posed by the complexity of turbulent pipe systems. Our framework begins with data-driven morphological exploration, aiming to find promising morphologies. It results in the shapes for selecting a reasonable number of candidates for the next shape refinement stage. Herein, we employ data-driven topology design, a gradient-free and multiobjective optimization methodology incorporating a deep generative model and the concept of evolutionary algorithms to generate promising arrangements. Subsequently, a deep clustering strategy extracts representative shapes. The final stage involves refining these shapes through shape optimization using a genetic algorithm. Applying the framework to a two-dimensional turbulent pipe system with a minimax objective shows its effectiveness in delivering high-performance solutions for the turbulent flow optimization problem with branching.
