scholarly journals High-quality mesh generation based on orthogonal software modules

Author(s):  
Josef Weinbub ◽  
Johann Cervenka ◽  
Karl Rupp ◽  
Siegfried Selberherr
Author(s):  
Keisuke Katsushima ◽  
Kohei Fujita ◽  
Tsuyoshi Ichimura ◽  
Muneo Hori ◽  
Lalith Maddegedara
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Author(s):  
Juraj Culak ◽  
Yulia V. Peet ◽  
David L. Chopp

A Matlab-based approach to image segmentation and mesh generation for creating high-quality hexagonal meshes is developed. The successful use of the procedure in patient-specific simulations of blood flow in a carotid artery is demonstrated.


2018 ◽  
Vol 75 (2) ◽  
pp. 582-595 ◽  
Author(s):  
Dawar Khan ◽  
Dong-Ming Yan ◽  
Yiqun Wang ◽  
Kaimo Hu ◽  
Juntao Ye ◽  
...  
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Author(s):  
Jie Pan ◽  
Jingwei Huang ◽  
Yunli Wang ◽  
Gengdong Cheng ◽  
Yong Zeng

Abstract Automatic generation of high-quality meshes is a base of CAD/CAE systems. The element extraction is a major mesh generation method for its capabilities to generate high-quality meshes around the domain boundary and to control local mesh densities. However, its widespread applications have been inhibited by the difficulties in generating satisfactory meshes in the interior of a domain or even in generating a complete mesh. The element extraction method's primary challenge is to define element extraction rules for achieving high-quality meshes in both the boundary and the interior of a geometric domain with complex shapes. This paper presents a self-learning element extraction system, FreeMesh-S, that can automatically acquire robust and high-quality element extraction rules. Two central components enable the FreeMesh-S: (1) three primitive structures of element extraction rules, which are constructed according to boundary patterns of any geometric boundary shapes; (2) a novel self-learning schema, which is used to automatically define and refine the relationships between the parameters included in the element extraction rules, by combining an Advantage Actor-Critic (A2C) reinforcement learning network and a Feedforward Neural Network (FNN). The A2C network learns the mesh generation process through random mesh element extraction actions using element quality as a reward signal and produces high-quality elements over time. The FNN takes the mesh generated from the A2C as samples to train itself for the fast generation of high-quality elements. FreeMesh-S is demonstrated by its application to two-dimensional quad mesh generation. The meshing performance of FreeMesh-S is compared with three existing popular approaches on ten pre-defined domain boundaries. The experimental results show that even with much less domain knowledge required to develop the algorithm, FreeMesh-S outperforms those three approaches in essential indices. FreeMesh-S significantly reduces the time and expertise needed to create high-quality mesh generation algorithms.


1999 ◽  
Vol 65 (638) ◽  
pp. 3288-3293
Author(s):  
Takashi YOKOHARI ◽  
Ichiro NISHIGAKI ◽  
Masayuki KAIHO ◽  
Masayuki HARIYA ◽  
Makoto ONODERA

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