Toward automatic CFD of flow through a blade passage using deep reinforcement learning

A method for generating an optimal mesh for a passage of an arbitrary blade is developed using deep reinforcement learning (DRL). Despite advances in the automation of mesh generation using empirical approaches or optimization algorithms, repeated tuning of meshing parameters remains necessary for each new configuration. The present method employs a DRL-based multi-condition optimization technique to define optimal meshing parameters as functions of the blade geometry and the flow condition, attaining automation, minimization of human intervention, and computational efficiency. An elliptic mesh generator, which requires manual tuning of meshing parameters, is developed to produce a structured mesh for a blade passage. Among these parameters, those controlling the mesh shape are optimized to maximize the geometric mesh quality. Subsequently, resolution-related meshing parameters are optimized by integrating the results from computational fluid dynamics simulations. After training, the mesh generator can create an optimal mesh for a new arbitrary configuration in a single try, eliminating repetitive parameter tuning. The effectiveness and robustness of the proposed method are demonstrated through the generation of meshes for various blade passages.