VGU RESEARCH REPOSITORY
Please use this identifier to cite or link to this item:
https://epub.vgu.edu.vn/handle/dlibvgu/749
DC Field | Value | Language |
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dc.contributor.author | Nguyen Khai Chau | en_US |
dc.date.accessioned | 2020-03-12T17:08:55Z | - |
dc.date.available | 2020-03-12T17:08:55Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | http://epub.vgu.edu.vn/handle/dlibvgu/749 | - |
dc.description.abstract | This thesis illustrates a polytree–based adaptive approach for local refnement in multi–material topology optimization (MMTOP) problems. The polytree mesh is introduced with its general adaptive data structure for arbitrary element types. In the optimization process, the adaptive mesh is generated automatically by the Centroidal Voronoi Tessellation (CVT) and error analysis. In order to resolve hanging nodes in element edges, the double mapping scheme is employed on a standard reference element to compute the shape function and their corresponding derivatives. For sharpening the material interfaces in a multi–level mesh, the so–called adaptive flter is released to improve the performance of the optimizer. The connection between multi–material topology optimization and additive manufacturing is also demonstrated to highlight a special role of numerical simulation in industrial production. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Vietnamese - German University | en_US |
dc.subject | Polytree | en_US |
dc.subject | Multi–material topology optimization | en_US |
dc.subject | Arbitrary element | en_US |
dc.title | A polytree-based adaptive polygonal fnite element method for multi-material topology optimization | en_US |
dc.type | Thesis | en_US |
item.grantfulltext | restricted | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | other | - |
Appears in Collections: | Computational Engineering (CompEng) |
Files in This Item:
File | Description | Size | Format | Existing users please Login |
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A polytree-based adaptive polygonal finite element method for multi-material topology optimization.pdf | 13.07 MB | Adobe PDF |
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