Gym-preCICE: Reinforcement learning environments for active flow control
Gym-preCICE: Reinforcement learning environments for active flow control
复制标题
Gym-preCICE：用于主动流量控制的强化学习环境
DOI：
10.1016/j.softx.2023.101446
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发表时间：
2023
期刊：
影响因子：
3.4
通讯作者：
Shams M
中科院分区：
文献类型：
--
作者：
Shams M
主动流量控制（AFC）涉及随时间操纵流体流量以实现期望的性能或效率。AFC作为一个顺序优化任务，可以从利用强化学习（RL）进行动态优化中受益。在这项工作中，我们介绍了Gym-preCICE，这是一个完全符合Gymnasium API的Python适配器，可以方便地为单物理和多物理AFC应用设计和开发RL环境。在参与者环境设置中，Gym-preCICE利用preCICE（一个用于分区多物理场仿真的开源耦合库）来处理控制器（参与者）和AFC仿真环境之间的信息交换。Gym-preCICE为RL和AFC的无缝非侵入式集成提供了一个框架，也为在各种AFC相关的工程应用中应用RL算法提供了一个平台。
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.