基于深度强化学习的叶片通道流动非迭代网格收敛解的最优网格生成
Optimal mesh generation for a non-iterative grid-converged solution of flow through a blade passage using deep reinforcement learning
AI总结：
本文针对叶片通道流动CFD分析开发了一种基于深度强化学习的自动网格生成方法，无需迭代调整参数，可在不同配置下确定最优参数，提升模拟精度与效率。
摘要：
本文开发了一种基于深度强化学习（DRL）的自动网格生成方法，用于叶片通道的最优计算流体动力学（CFD）分析。与需要为每个新几何形状和流动条件重复调整网格参数的传统自动化技术不同，本文开发的方法训练网格生成器以非迭代方式确定不同配置下的最优参数。首先，优化控制网格形状的参数以最大化几何网格质量，该质量通过雅可比矩阵的行列式比和偏斜度来衡量。随后，通过结合CFD结果优化控制分辨率的参数。采用多智能体强化学习，使256个智能体并行构建网格并对随机分配的流动配置进行CFD分析，旨在多目标优化框架内实现最大的模拟精度和计算效率。训练后，网格生成器能够为新配置生成在所需计算成本下产生收敛解的网格，从而消除了网格收敛所需的迭代CFD程序。在各种叶片通道配置中研究了该方法的鲁棒性和有效性，包括高压和低压涡轮叶片、轴流压缩机叶片和冲击转子叶片等多种叶片几何形状。此外，该方法能够为包括边界层、冲击波和流动分离等复杂现象在内的多种流动条件确定最优网格分辨率。通过将单次尝试获得的精度和效率与传统迭代优化方法的结果进行比较，证实了其最优性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
