构建流体控制通用平台，让强化学习跨场景高效减阻。
The HydroGym Reinforcement Learning Platform for Fluid Dynamics
- 开发与求解器无关的流体控制平台HydroGym，支持多种数值方法。
- 在典型构型中实现超90%的阻力降低，零样本迁移达38%减阻。
- 适合流体力学、强化学习及控制领域的研究者使用。
流体建模与控制在科学与工程中至关重要。有效流控可提升升力、降低阻力、增强混合并抑制噪声，有望催生新技术。然而流体系统高度非线性且多尺度，控制难度大。尽管强化学习在机器人和蛋白质折叠中取得突破，但流体领域仍缺乏统一基准：控制器通常仅适用于单一几何与工况，难以积累、迁移与比较。本文提出HydroGym，一个求解器无关的流体控制强化学习平台，通过标准化基础设施实现跨流态的可转移控制智能。平台提供61个以上经验证的环境，覆盖从层流到湍流，雷诺数最高达Re=400,000，涵盖二维与三维流动。支持有限体积、谱元法、有限元、格子玻尔兹曼及全可微分求解器，适用于梯度增强优化。在各类环境中，强化学习代理持续发现鲁棒控制机制，如边界层调控、声反馈干扰与尾迹重构，在典型配置中阻力降幅超90%。关键成果在于零样本迁移：仅在简化通道流训练的代理，在未见过的三维机翼段（弦长雷诺数Re=200,000）实现38%摩擦阻力降低，探索成本减少四个数量级。这表明代理捕捉的是本质物理规律而非特定配置模式，指向可泛化的控制策略。HydroGym为流体力学、机器学习与控制研究提供可扩展的社区基础设施。
原文摘要 · Abstract (English)
Modeling and controlling fluids is critical across science and engineering. Effective flow control can increase lift, reduce drag, enhance mixing, and attenuate noise, potentially unlocking new technologies. Yet controlling fluids is hard: the dynamics are high-dimensional, nonlinear, and multiscale. While reinforcement learning (RL) has recently succeeded in robotics and protein folding through shared benchmarks, fluid dynamics has resisted such progress: each controller is typically tuned to a single geometry and operating point, making results hard to accumulate, transfer, and compare. We introduce HydroGym, a solver-independent RL platform for flow control, and show that standardized infrastructure unlocks transferable control intelligence across flow regimes. HydroGym provides 61+ validated environments spanning laminar to turbulent flows, with systematic Reynolds number progressions up to Re=400,000 and Mach number variations in 2D and 3D. It supports diverse backends, including finite-volume, spectral-element, finite-element, lattice-Boltzmann, and fully differentiable solvers for gradient-enhanced optimization. Across environments, RL agents consistently discover robust control principles, such as boundary-layer manipulation, acoustic-feedback disruption, and wake reorganization, yielding drag reductions exceeding 90% in canonical configurations. Critically, we demonstrate zero-shot transfer: agents trained only on a simplified channel flow achieve 38% friction-drag reduction on an unseen 3D wing section at chord Reynolds number Re=200,000 reducing exploration costs by four orders of magnitude versus direct on-wing optimization. This suggests RL agents uncover essential physics rather than configuration-specific patterns, pointing toward generalizable control. HydroGym offers extensible, scalable community infrastructure for fluid dynamics, machine learning, and control research.
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