Closed-loop separated flow con on backward facing step

Abstract

Closed-loop separated flow control on backward facing step This thesis proposes a novel approach to closed-loop flow control using artificial intelligence (AI), focusing on the prediction and manipulation of dynamic flow behaviors in fluid dynamics applications. The research begins with a numerical investigation of flow separation over a backward-facing step (BFS) using the Detached Eddy Simulation (DES) model, validated against experimental data to ensure fidelity in capturing critical flow features. We introduce a new class of neural networks designed to predict dynamic flow behavior over the (BFS) using wall pressure measurements. These networks achieve high accuracy, enabling real-time flow control applications. The study further explores the influence of frequency information and shear layer dynamics on effective flow control strategies. Leveraging these insights, AI-driven methods are applied to predict and manipulate shear layer behavior, achieving significant improvements in flow control performance. Correlation analyses demonstrate the predictability of future and past flow states within a defined time horizon, offering valuable insights for optimizing both open-loop and closed-loop control systems. These findings contribute to the development of adaptive, efficient flow control strategies with broad implications for aerospace, automotive, and energy systems.