Gym-preCICE: Reinforcement learning environments for active flow control

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...

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Bibliographic Details
Published in:SoftwareX 2023-07, Vol.23, p.101446, Article 101446
Main Authors: Shams, Mosayeb, Elsheikh, Ahmed H.
Format: Article
Language:English
Subjects:
Active flow control
Gymnasium
OpenAI Gym
preCICE
Reinforcement learning
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Summary: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.
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2023.101446