Data-Driven Morphological Exploration and Shape Optimization for Turbulent Pipe Systems

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 article, 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 multi-objective 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.