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DRLinSPH: an open-source platform using deep reinforcement learning and SPHinXsys for fluid-structure-interaction problems
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DOI：10.1080/19942060.2025.2460677.png)
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Abstract
En 中文
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. Furthermore, the training of DRL models necessitates a stable numerical environment, particularly for FSI problems. 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. This work presents 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 by controlling displacement or introducing deformation, achieving a maximum wave height reduction of 68.81% and 42.92%, respectively; wave energy harvesting optimization with an 8.25% improvement through an oscillating wave surge converter (OWSC) by regulating the damping characteristics of the Power Take-Off (PTO) system; and muscle-driven fish swimming control in a straight line within vortices. The results demonstrate the platform's accuracy, stability, and scalability, highlighting its potential to advance industrial solutions for complex FSI challenges.
Keywords:
Smoothed particle hydrodynamics
fluid-structure interaction
deep reinforcement learning
sloshing suppression
oscillating wave surge converter
fish swimming
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