Gym-preCICE: Reinforcement learning environments for active flow control

Abstract

Active flow control (AFC) involves manipulating fluid flow over time to achieve a desired performance or efficiency. AFC, as a sequential optimisation task, can benefit from utilising Reinforcement Learning (RL) for dynamic optimisation. In this work, we introduce Gym-preCICE, a Python adapter fully compliant with Gymnasium API to facilitate designing and developing RL environments for single- and multi-physics AFC applications. In an actor–environment setting, Gym-preCICE takes advantage of preCICE, an open-source coupling library for partitioned multi-physics simulations, to handle information exchange between a controller (actor) and an AFC simulation environment. Gym-preCICE provides a framework for seamless non-invasive integration of RL and AFC, as well as a playground for applying RL algorithms in various AFC-related engineering applications.

Keywords

• Reinforcement learning
• Active flow control
• Gymnasium
• OpenAI Gym
• preCICE