SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918110130339840 |
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| author | Xiao, Maochao Wang, Yuning Rodach, Felix Font, Bernat Kurz, Marius Suárez, Pol Zhou, Di Alcántara-Ávila, Francisco Zhu, Ting Liu, Junle Montalà, Ricard Chen, Jiawei Rabault, Jean Lehmkuhl, Oriol Beck, Andrea Larsson, Johan Vinuesa, Ricardo Pirozzoli, Sergio |
| author_facet | Xiao, Maochao Wang, Yuning Rodach, Felix Font, Bernat Kurz, Marius Suárez, Pol Zhou, Di Alcántara-Ávila, Francisco Zhu, Ting Liu, Junle Montalà, Ricard Chen, Jiawei Rabault, Jean Lehmkuhl, Oriol Beck, Andrea Larsson, Johan Vinuesa, Ricardo Pirozzoli, Sergio |
| contents | Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and discovery of novel numerical schemes. We introduce SmartFlow, a CFD-solver-agnostic framework for both single- and multi-agent DRL algorithms that can easily integrate with MPI-parallel CPU and GPU-accelerated solvers. Built on Relexi and SmartSOD2D, SmartFlow uses the SmartSim infrastructure library and our newly developed SmartRedis-MPI library to enable asynchronous, low-latency, in-memory communication between CFD solvers and Python-based DRL algorithms. SmartFlow leverages PyTorch's Stable-Baselines3 for training, which provides a modular, Gym-like environment API. We demonstrate its versatility via three case studies: single-agent synthetic-jet control for drag reduction in a cylinder flow simulated by the high-order FLEXI solver, multi-agent cylinder wake control using the GPU-accelerated spectral-element code SOD2D, and multi-agent wall-model learning for large-eddy simulation with the finite-difference solver CaLES. SmartFlow's CFD-solver-agnostic design and seamless HPC integration is promising to accelerate RL-driven fluid-mechanics studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00645 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms Xiao, Maochao Wang, Yuning Rodach, Felix Font, Bernat Kurz, Marius Suárez, Pol Zhou, Di Alcántara-Ávila, Francisco Zhu, Ting Liu, Junle Montalà, Ricard Chen, Jiawei Rabault, Jean Lehmkuhl, Oriol Beck, Andrea Larsson, Johan Vinuesa, Ricardo Pirozzoli, Sergio Fluid Dynamics Computational Physics Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and discovery of novel numerical schemes. We introduce SmartFlow, a CFD-solver-agnostic framework for both single- and multi-agent DRL algorithms that can easily integrate with MPI-parallel CPU and GPU-accelerated solvers. Built on Relexi and SmartSOD2D, SmartFlow uses the SmartSim infrastructure library and our newly developed SmartRedis-MPI library to enable asynchronous, low-latency, in-memory communication between CFD solvers and Python-based DRL algorithms. SmartFlow leverages PyTorch's Stable-Baselines3 for training, which provides a modular, Gym-like environment API. We demonstrate its versatility via three case studies: single-agent synthetic-jet control for drag reduction in a cylinder flow simulated by the high-order FLEXI solver, multi-agent cylinder wake control using the GPU-accelerated spectral-element code SOD2D, and multi-agent wall-model learning for large-eddy simulation with the finite-difference solver CaLES. SmartFlow's CFD-solver-agnostic design and seamless HPC integration is promising to accelerate RL-driven fluid-mechanics studies. |
| title | SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms |
| topic | Fluid Dynamics Computational Physics |
| url | https://arxiv.org/abs/2508.00645 |