Minisymposium Presentation

Relexi is a powerful tool that allows to use existing simulation codes as training environments for reinforcement learning (RL) on high-performance computing (HPC) systems. This framework allows to apply RL to problems typically requiring HPC hardware such as computational fluid dynamics (CFD) or related fields. For this, Relexi applies the SmartSim library, which allows to manage the individual simulation environments on HPC systems and provides an efficient communication channel between itself and the simulation code. In this talk, we demonstrate two specific applications for the use of RL in CFD. First, we apply the framework to a task in active flow control. Here, the RL agent is trained to minimize the drag for the flow around a two-dimensional cylinder using blowing and suction jets at the cylinder’s poles. For this case, the agent is demonstrated to reduce the experienced drag by about 15%. Moreover, Relexi is applied to the task of turbulence modeling in large eddy simulation, where it was found to outperform traditional models, while being robust against changes in resolution, Reynolds number, and also when applied to heavily deformed meshes.