利用深度强化学习开发基于代理的流动分析网格生成器

IF 8.7 2区 工程技术 Q1 Mathematics
Keunoh Lim, Kyungjae Lee, Sanga Lee, Kwanjung Yee
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引用次数: 0

摘要

计算流体动力学(CFD)广泛应用于科研和工业领域。网格的质量,尤其是边界层的网格质量,对 CFD 的精度有很大影响。尽管其重要性不言而喻,但网格生成过程仍然是人工操作,耗时耗力,还可能引入错误和不一致性。传统方法的局限性促使人们最近开始探索用于网格生成的深度强化学习(DRL)。虽然一些研究已经证明了 DRL 在网格生成中的适用性,但它们在利用现有工具方面存在局限性,因此未能充分发挥 DRL 的潜力。本研究提出了一种利用 DRL 生成边界网格的新方法,即基于代理的网格生成器。曲面上的节点充当代理,优化进入空间的路径,从而生成高质量的网格。由于其计算性质和执行的确定性,网格生成自然适合 DRL。然而,挑战也随之而来,包括同时训练众多代理以及管理它们在广阔状态空间中的相互依赖关系。在本研究中,将网格生成作为 DRL 任务(定义状态、代理、行动和奖励)后,对最佳学习条件进行了研究,从而解决了这些难题。得出的最佳条件被应用于生成二维机翼网格,以验证所提方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Development of agent-based mesh generator for flow analysis using deep reinforcement learning

Development of agent-based mesh generator for flow analysis using deep reinforcement learning

Computational fluid dynamics (CFD) has widespread application in research and industry. The quality of the mesh, particularly in the boundary layer, significantly influences the CFD accuracy. Despite its importance, the mesh generation process remains manual and time intensive, with the introduction of potential errors and inconsistencies. The limitations of traditional methods have prompted the recent exploration of deep reinforcement learning (DRL) for mesh generation. Although some studies have demonstrated the applicability of DRL in mesh generation, they have limitations in utilizing existing tools, thereby falling short of fully leveraging the potential of DRL. This study proposes a new boundary mesh generation method using DRL, namely an agent-based mesh generator. The nodes on the surface act as agents and optimize the paths into space to create high-quality meshes. Mesh generation is naturally suited to DRL owing to its computational nature and deterministic execution. However, challenges also arise, including training numerous agents simultaneously and managing their interdependencies in a vast state space. In this study, these challenges are addressed along with an investigation of the optimal learning conditions after formulating grid generation as a DRL task: defining states, agents, actions, and rewards. The derived optimal conditions are applied to generate two dimensional airfoil grids to validate the feasibility of the proposed approach.

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来源期刊
Engineering with Computers
Engineering with Computers 工程技术-工程:机械
CiteScore
16.50
自引率
2.30%
发文量
203
审稿时长
9 months
期刊介绍: Engineering with Computers is an international journal dedicated to simulation-based engineering. It features original papers and comprehensive reviews on technologies supporting simulation-based engineering, along with demonstrations of operational simulation-based engineering systems. The journal covers various technical areas such as adaptive simulation techniques, engineering databases, CAD geometry integration, mesh generation, parallel simulation methods, simulation frameworks, user interface technologies, and visualization techniques. It also encompasses a wide range of application areas where engineering technologies are applied, spanning from automotive industry applications to medical device design.
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