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Computational Mechanics
Erschienen in: Computational Mechanics 6/2017

13.02.2017 | Original Paper

Geometric constraints for shape and topology optimization in architectural design

verfasst von: Charles Dapogny, Alexis Faure, Georgios Michailidis, Grégoire Allaire, Agnes Couvelas, Rafael Estevez

Erschienen in: Computational Mechanics | Ausgabe 6/2017

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Abstract

This work proposes a shape and topology optimization framework oriented towards conceptual architectural design. A particular emphasis is put on the possibility for the user to interfere on the optimization process by supplying information about his personal taste. More precisely, we formulate three novel constraints on the geometry of shapes; while the first two are mainly related to aesthetics, the third one may also be used to handle several fabrication issues that are of special interest in the device of civil structures. The common mathematical ingredient to all three models is the signed distance function to a domain, and its sensitivity analysis with respect to perturbations of this domain; in the present work, this material is extended to the case where the ambient space is equipped with an anisotropic metric tensor. Numerical examples are discussed in two and three space dimensions.
Vorheriger Artikel An effective meshfree reproducing kernel method for buckling analysis of cylindrical shells with and without cutouts
Nächster Artikel A multi-material topology optimization approach for wrinkle-free design of cable-suspended membrane structures

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Fußnoten
1
The unpublished work carried out in this reference was obtained with the collaboration of some of the authors, using the exact same algorithms as those detailed in this article
 
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Metadaten
Titel
Geometric constraints for shape and topology optimization in architectural design
verfasst von
Charles Dapogny
Alexis Faure
Georgios Michailidis
Grégoire Allaire
Agnes Couvelas
Rafael Estevez
Publikationsdatum
13.02.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Computational Mechanics / Ausgabe 6/2017
Print ISSN: 0178-7675
Elektronische ISSN: 1432-0924
DOI
https://doi.org/10.1007/s00466-017-1383-6

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