Fluid-structure interaction (FSI) problems are characterized by strong nonlinearities arising from complex interactions between fluids and structures. These pose significant challenges for traditional control strategies in optimizing structural motion, often leading to suboptimal performance. In contrast, deep reinforcement learning (DRL), through agent interactions within numerical simulation environments and the approximation of control policies using deep neural networks (DNNs), has shown considerable promise in addressing high-dimensional FSI problems. Additionally, smoothed particle hydrodynamics (SPH) offers a flexible and efficient computational approach for modeling large deformations, fractures, and complex interface movements inherent in FSI, outperforming traditional grid-based methods. In this work, we present DRLinSPH, an open-source Python platform that integrates the SPH-based numerical environment provided by the open-source software SPHinXsys with the mature DRL platform Tianshou to enable parallel training for FSI problems. DRLinSPH has been successfully applied to four FSI scenarios: sloshing suppression using rigid and elastic baffles, optimization of wave energy capture through an oscillating wave surge converter (OWSC), and muscle-driven fish swimming in vortices. The results demonstrate the platform's accuracy, stability, and scalability, highlighting its potential to advance industrial solutions for complex FSI challenges.

翻译：流固耦合（FSI）问题具有强烈的非线性特征，这些非线性源于流体与结构之间复杂的相互作用。这对优化结构运动的传统控制策略提出了重大挑战，往往导致性能欠佳。相比之下，深度强化学习（DRL）通过在数值模拟环境中进行智能体交互，并利用深度神经网络（DNN）逼近控制策略，在解决高维FSI问题上显示出巨大潜力。此外，光滑粒子流体动力学（SPH）为模拟FSI中固有的大变形、断裂及复杂界面运动提供了一种灵活高效的计算方法，其性能优于传统的基于网格的方法。本文提出了DRLinSPH，这是一个开源Python平台。该平台将开源软件SPHinXsys提供的基于SPH的数值环境与成熟的DRL平台Tianshou相结合，实现了针对FSI问题的并行训练。DRLinSPH已成功应用于四个FSI场景：使用刚性和弹性挡板抑制晃荡、通过振荡波涌转换器（OWSC）优化波浪能捕获，以及在涡流中肌肉驱动的鱼类游动。结果证明了该平台的准确性、稳定性和可扩展性，突显了其在推动复杂FSI挑战的工业解决方案方面的潜力。