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A feature-aware SPH for isotropic unstructured mesh generation

机译:用于各向同性非结构化网格生成的特征感知SPH

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In this paper, we present a feature-aware SPH method for the concurrent and automated isotropic unstructured mesh generation. Two additional objectives are achieved with the proposed method compared to the original SPH-based mesh generator (Fu et al., 2019). First, a feature boundary correction term is introduced to address the issue of incomplete kernel support at the boundary vicinity. The mesh generation of feature curves, feature surfaces and volumes can be handled concurrently without explicitly following a dimensional sequence. Second, a two-phase model is proposed to characterize the mesh-generation procedure by a feature-size-adaptation phase and a mesh-quality-optimization phase. By proposing a new error measurement criterion and an adaptive control system with two sets of simulation parameters, the objectives of faster feature-size adaptation and local mesh-quality improvement are merged into a consistent framework. The proposed method is validated with a set of 2D and 3D numerical tests with different complexities and scales. The results demonstrate that high-quality meshes are generated with a significant speedup of convergence. (C) 2020 Elsevier B.V. All rights reserved.
机译:在本文中,我们提出了一种用于并发和自动各向同性非结构化网格生成的特征感知的SPH方法。与基于SPH的网格发生器相比,通过所提出的方法实现了两种额外的目标(FU等,2019)。首先,引入了一个特征边界校正项来解决边界附近不完整内核支持的问题。可以同时处理特征曲线,特征曲面和卷的网格生成,而不会在尺寸序列之后明确地处理。其次,提出了一种两相模型来通过特征大小适应阶段和网格质量优化阶段来表征网格生成过程。通过提出具有两组仿真参数的新的误差测量标准和自适应控制系统,可以将更快的特征大小适应和本地网格质量改进的目标合并为一致的框架。该方法用具有不同复杂性和尺度的一组2D和3D数值测试验证。结果表明,具有显着加速的收敛性的高质量网格。 (c)2020 Elsevier B.v.保留所有权利。

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