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Title: An Introduction to Automatic Mesh Generation Algorithms - Part I.

Abstract

Abstract not provided.

Authors:
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1394106
Report Number(s):
SAND2016-9194C
647475
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the 25th International Meshing Roundtable held September 26-30, 2016 in Washington, DC.
Country of Publication:
United States
Language:
English

Citation Formats

Owen, Steven J. An Introduction to Automatic Mesh Generation Algorithms - Part I.. United States: N. p., 2016. Web.
Owen, Steven J. An Introduction to Automatic Mesh Generation Algorithms - Part I.. United States.
Owen, Steven J. 2016. "An Introduction to Automatic Mesh Generation Algorithms - Part I.". United States. doi:. https://www.osti.gov/servlets/purl/1394106.
@article{osti_1394106,
title = {An Introduction to Automatic Mesh Generation Algorithms - Part I.},
author = {Owen, Steven J.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 9
}

Conference:
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  • Abstract not provided.
  • One of the inconveniences associated with the existing finite-element packages is the need for an educated user to develop a correct mesh at the preprocessing level. Procedures which start with a coarse mesh and attempt serious refinements, as is the case in most adaptive finite-element packages, are time consuming and costly. Hence, it is very important to develop a tool that can provide a mesh that either leads immediately to an acceptable solution, or would require fewer correcting steps to achieve better results. In this paper, the authors present a technique for automatic mesh generation based on artificial neural networksmore » (ANN). The essence of this technique is to predict the mesh density distribution of a given model, and then supply this information to a Kohonen neural network which provides the final mesh. Prediction of mesh density is accomplished by a simple feedforward neural network which has the ability to learn the relationship between mesh density and model geometric features. It will be shown that ANN are able to recognize delicate areas where a sharp variation of the magnetic field is expected. Examples of 2-D models are provided to illustrate the usefulness of the proposed technique.« less
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  • Mesh generation has remained one of the most serious bottlenecks in solidification simulation by finite elements. In the present study, an approach using a combined extended octree/advancing front algorithm is suggested. Using this method, automatic mesh generation can easily take account of the special demands of the casting and solidification process, i.e.: fine enmeshment near material boundaries between melt and dies, rough meshes in e.g. moulds and dies. The creation of the finite element mesh for a single casting or a complete casting system is carried out in four steps. First, a solid model of the casting system is builtmore » using an arbitrary commercial solid modeler. In the second step this solid model is converted into a so-called extended octree. The third step consists of the generation of surface meshes: in each of its final octants--the so-called leaves of the octree--a mesh of triangles will be generated on the object surfaces inside the octants and on the borders of the octants. Finally, in the fourth step, an advancing front algorithm is used to create leaf by leaf a 3-dimensional mesh of tetrahedrons.« less