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 (formerly known as OpenAI Gym) 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. The developed framework results in a seamless non-invasive integration of realistic physics-based simulation toolboxes with RL algorithms. Gym-preCICE provides a framework for designing RL environments to model AFC tasks, as well as a playground for applying RL algorithms in various AFC-related engineering applications.

翻译：主动流动控制（AFC）涉及随时间变化操纵流体运动，以实现期望的性能或效率。作为一项序列优化任务，AFC可利用强化学习（RL）进行动态优化。本文提出Gym-preCICE——一个完全兼容Gymnasium（原OpenAI Gym）API的Python适配器，旨在促进单物理场与多物理场AFC应用中强化学习环境的设计与开发。在智能体-环境框架中，Gym-preCICE借助开源耦合库preCICE处理分区多物理场仿真的信息交换，实现控制器（智能体）与AFC仿真环境之间的数据交互。该框架实现了基于真实物理的仿真工具箱与强化学习算法的无缝非侵入式集成。Gym-preCICE不仅为建模AFC任务的强化学习环境提供了设计框架，也为在各类AFC相关工程应用中部署强化学习算法搭建了实验平台。