论文标题
使用深钢筋学习的非著作生成刀片通道的最佳网格
Non-iterative generation of an optimal mesh for a blade passage using deep reinforcement learning
论文作者
论文摘要
开发了一种使用深钢筋学习（DRL）来非静脉化的方法，为任意叶片通道生成最佳网格。尽管使用经验方法或优化算法在网格生成中自动化，但新几何形状仍然需要重复调整网格划分参数。本文开发的方法采用基于DRL的多条件优化技术来定义最佳的网格划分参数，这是刀片几何形状的函数，达到自动化，人工干预的最小化和计算效率。通过训练椭圆网的生成器来优化网格划分参数，该椭圆网生成具有任意叶片几何形状的刀片通道的结构化网格。在DRL过程的每一集中，对网格发电机进行了训练，以通过更新网格升级参数来生成最佳的网格，直到通过雅各布矩阵的决定因素和偏心度的比率来衡量的网格质量达到最高水平。训练完成后，网格生成器将在一次尝试中为新的任意刀片通过的最佳网格创建一个最佳网格，而无需重复过程，即从刮擦中为网格生成的参数调整。提出方法的有效性和鲁棒性通过各种叶片通道的网格产生来证明。
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.
