In aerodynamic design, high-quality multiblock mesh generation serves as the cornerstone for accurate lift-drag analysis of wings. However, conventional computational fluid dynamics (CFD) workflows offered by industrial software reveals critical limitations in labor-intensive manual operations. While automatic meshing in 2D wing cross-section is well-established, its extension to 3D wing meshing faces unresolved challenges, including excessive block generation, complex feature alignment, and incomplete simulation-ready pipelines. This paper presents an automatic multiblock mesh generation framework specifically targeting these commercial software shortcomings for 3D wing analysis. Our methodology addresses three persistent challenges: (1) a parameterization-based tip surface meshing method with optimized cell quality, (2) an extrusion-based wing body meshing approach ensuring precise feature alignment at joints and leading edges, and (3) an end-to-end pipeline from geometric pre-processing to CFD-ready multiblock mesh generation. Experiments demonstrate the framework’s ability to produce high-quality meshes with minimal manual intervention, streamlining the workflow for aerodynamic simulations.

Reinforcement Learning (RL) has emerged as a promising data-driven solution for wargaming decision-making. However, two domain challenges still exist: (1) dealing with discrete-continuous hybrid wargaming control and (2) accelerating RL deployment with rich offline data. Existing RL methods fail to handle these two issues simultaneously, thereby we propose a novel offline RL method targeting hybrid action space. A new constrained action representation technique is developed to build a bidirectional mapping between the original hybrid action space and a latent space in a semantically consistent way. This allows learning a continuous latent policy with offline RL with better exploration feasibility and scalability and reconstructing it back to a needed hybrid policy. Critically, a novel offline RL optimization objective with adaptively adjusted constraints is designed to balance the alleviation and generalization of out-of-distribution actions. Our method demonstrates superior performance and generality across different tasks, particularly in typical realistic wargaming scenarios.