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基于机器学习的非结构网格阵面推进生成技术初探

王年华 鲁鹏 常兴华 张来平

王年华, 鲁鹏, 常兴华, 张来平. 基于机器学习的非结构网格阵面推进生成技术初探[J]. 力学学报, 2021, 53(3): 740-751. doi: 10.6052/0459-1879-20-402
引用本文: 王年华, 鲁鹏, 常兴华, 张来平. 基于机器学习的非结构网格阵面推进生成技术初探[J]. 力学学报, 2021, 53(3): 740-751. doi: 10.6052/0459-1879-20-402
Wang Nianhua, Lu Peng, Chang Xinghua, Zhang Laiping. PRELIMINARY INVESTIGATION ON UNSTRUCTURED MESH GENERATION TECHNIQUE BASED ON ADVANCING FRONT METHOD AND MACHINE LEARNING METHODS[J]. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(3): 740-751. doi: 10.6052/0459-1879-20-402
Citation: Wang Nianhua, Lu Peng, Chang Xinghua, Zhang Laiping. PRELIMINARY INVESTIGATION ON UNSTRUCTURED MESH GENERATION TECHNIQUE BASED ON ADVANCING FRONT METHOD AND MACHINE LEARNING METHODS[J]. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(3): 740-751. doi: 10.6052/0459-1879-20-402

基于机器学习的非结构网格阵面推进生成技术初探

doi: 10.6052/0459-1879-20-402
基金项目: 1) 国家重点研发计划(2016YFB0200701);空气动力学国家重点实验室创新基金(SKLA190104);国家重大专项(GJXM92579)
详细信息
    作者简介:

    2) 王年华,助理研究员,主要研究方向: 计算流体力学. E-mail: nhwang@skla.cardc.cn

    通讯作者:

    王年华

  • 中图分类号: V211.3

PRELIMINARY INVESTIGATION ON UNSTRUCTURED MESH GENERATION TECHNIQUE BASED ON ADVANCING FRONT METHOD AND MACHINE LEARNING METHODS

  • 摘要: 网格生成和自适应是制约计算流体力学未来发展的瓶颈问题之一,网格生成自动化和智能化仍是一个需要持续研究的领域.随着高性能计算算力的提升和大数据时代的到来,以机器学习为代表的人工智能方法已经成功应用于包括流体力学在内的多个领域,革命性地推动了这些领域的发展.本文首先简要综述机器学习方法在非结构网格生成领域的研究进展,分析基于机器学习进行非结构网格生成的关键问题;其次,设计非结构网格样本数据格式并实现了样本数据集的自动提取,通过结合人工神经网络和阵面推进法,初步发展了一种基于人工神经网络的二维非结构网格阵面推进生成方法;最后,采用新发展的方法生成了几个典型二维各向同性非结构三角形网格(二维圆柱、二维NACA0012翼型和30p30n三段翼型),进一步采用合并法生成了相应的三角形/四边形混合网格,并测试了网格质量和生成耗时,结果显示本文方法生成的网格质量可以达到商业软件的水平,且生成效率较传统阵面推进法提高30%.

     

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  • 收稿日期:  2020-11-27
  • 刊出日期:  2021-03-10

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