基于深度强化学习的叶片通道最优网格非迭代生成
Non-iterative generation of an optimal mesh for a blade passage using deep reinforcement learning
AI总结：
本文提出基于深度强化学习的非迭代网格生成方法，通过训练椭圆网格生成器自动优化参数，为任意叶片通道一次性生成高质量网格，减少人工干预并提高效率。
摘要：
本文开发了一种利用深度强化学习（DRL）非迭代地为任意叶片通道生成最优网格的方法。尽管使用经验方法或优化算法实现了网格生成的自动化，但对于新几何形状，仍然需要反复调整网格参数。本文所提出的方法采用基于DRL的多条件优化技术，将最优网格参数定义为叶片几何形状的函数，从而实现自动化、减少人工干预并提高计算效率。通过训练椭圆网格生成器来优化网格参数，该生成器可为具有任意叶片几何形状的叶片通道生成结构化网格。在DRL过程的每一回合中，网格生成器通过更新网格参数来为随机选择的叶片通道生成最优网格，直到网格质量（以雅可比矩阵行列式之比和偏斜度衡量）达到最高水平。训练完成后，网格生成器可一次性为新的任意叶片通道生成最优网格，无需从头开始进行重复的参数调整过程。通过为各种叶片通道生成网格，验证了所提方法的有效性和鲁棒性。
英文摘要：
A method using deep reinforcement learning (DRL) to non-iteratively generate an optimal mesh for an arbitrary blade passage is developed. Despite automation in mesh generation using either an empirical approach or an optimization algorithm, repeated tuning of meshing parameters is still required for a new geometry. The method developed herein employs a DRL-based multi-condition optimization technique to define optimal meshing parameters as a function of the blade geometry, attaining automation, minimization of human intervention, and computational efficiency. The meshing parameters are optimized by training an elliptic mesh generator which generates a structured mesh for a blade passage with an arbitrary blade geometry. During each episode of the DRL process, the mesh generator is trained to produce an optimal mesh for a randomly selected blade passage by updating the meshing parameters until the mesh quality, as measured by the ratio of determinants of the Jacobian matrices and the skewness, reaches the highest level. Once the training is completed, the mesh generator create an optimal mesh for a new arbitrary blade passage in a single try without an repetitive process for the parameter tuning for mesh generation from the scratch. The effectiveness and robustness of the proposed method are demonstrated through the generation of meshes for various blade passages.
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。
