  An automatic mesh generation method for optimal computational fluid dynamics
(CFD) analysis of a blade passage is developed using deep reinforcement
learning (DRL). Unlike conventional automation techniques, which require
repetitive tuning of meshing parameters for each new geometry and flow
condition, the method developed herein trains a mesh generator to determine
optimal parameters across varying configurations in a non-iterative manner.
Initially, parameters controlling mesh shape are optimized to maximize
geometric mesh quality, as measured by the ratio of determinants of Jacobian
matrices and skewness. Subsequently, resolution-controlling parameters are
optimized by incorporating CFD results. Multi-agent reinforcement learning is
employed, enabling 256 agents to construct meshes and perform CFD analyses
across randomly assigned flow configurations in parallel, aiming for maximum
simulation accuracy and computational efficiency within a multi-objective
optimization framework. After training, the mesh generator is capable of
producing meshes that yield converged solutions at desired computational costs
for new configurations in a single simulation, thereby eliminating the need for
iterative CFD procedures for grid convergence. The robustness and effectiveness
of the method are investigated across various blade passage configurations,
accommodating a range of blade geometries, including high-pressure and
low-pressure turbine blades, axial compressor blades, and impulse rotor blades.
Furthermore, the method is capable of identifying the optimal mesh resolution
for diverse flow conditions, including complex phenomena like boundary layers,
shock waves, and flow separation. The optimality is confirmed by comparing the
accuracy and the efficiency achieved in a single attempt with those from the
conventional iterative optimization method.
