• DocumentCode
    1797758
  • Title

    A mesh generation method for geometric features based on knowledge-based engineering

  • Author

    Heng Liu ; Ping Xi

  • Author_Institution
    Sch. of Mech. Eng. & Autom., BeiHang Univ., Beijing, China
  • fYear
    2014
  • fDate
    15-17 Nov. 2014
  • Firstpage
    425
  • Lastpage
    430
  • Abstract
    Although many mesh generation methods have been developed, difficulties still exist in generating high-quality structured hex-meshes for specific local geometric features such as holes and columns in three-dimensional models. In this paper, a mesh generation approach is proposed to overcome the problem of meshing local features having regular structure with high quality. Firstly, analyze the feature´s geometry information and customize its mesh process. Secondly, introduce parametric design into the optimization of mesh customization. Scaled Jacobian and aspect ratio are taken as indexes of the objective functions. Thirdly, an optimized module of the feature is set up by integrating the meshing scheme into program. By establishing a knowledge base of optimized modules corresponding to geometric features, a local feature can be automatically meshed according to its geometry information. Experiments in this paper reveal that the method is applicable and stable for generating high-quality meshes for local geometric features with regular structure.
  • Keywords
    feature extraction; knowledge based systems; mesh generation; geometric features; knowledge-based engineering; mesh customization optimization; mesh generation method; Blades; Finite element analysis; Geometry; Jacobian matrices; Mesh generation; Optimization; Turbines; hexahedral mesh; knowledge-based engineering; local feature; optimized module;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems and Informatics (ICSAI), 2014 2nd International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4799-5457-5
  • Type

    conf

  • DOI
    10.1109/ICSAI.2014.7009326
  • Filename
    7009326