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Structural and Multidisciplinary Optimization

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01.03.2011 | Research Paper

A two phase approach based on skeleton convergence and geometric variables for topology optimization using genetic algorithm

verfasst von: R. Balamurugan, C. V. Ramakrishnan, N. Swaminathan

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 3/2011

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Abstract

This paper introduces a set of skeleton operators for characterizing topologies evolving in a bit-array represented structural topology optimization problem. It is shown that the design generally converges to a stable skeleton fairly early in the optimization process. It is observed that further optimization is more about finding optimal gross shape for the various branches of the converged skeleton and the bit-array representation is not appropriate. A two-phase approach to topology optimization is proposed in which the first phase, where bit-array is used to represent the topology, ends with the detection of stabilization of skeleton, and the second phase proceeds further with the geometry based representation that directly addresses gross variation in shape of the branches of the converged skeleton. Genetic Algorithm has been used for optimization in both the phases. The efficiency and effectiveness of the use of skeleton operators and geometric variables for identification of convergence in the first phase and optimization in the second phase respectively is demonstrated.

Vorheriger Artikel Topology optimization of continuum structures with different tensile and compressive properties in bridge layout design

Nächster Artikel On the effect of self-penalization of piezoelectric composites in topology optimization

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Titel: A two phase approach based on skeleton convergence and geometric variables for topology optimization using genetic algorithm
verfasst von: R. Balamurugan
C. V. Ramakrishnan
N. Swaminathan
Publikationsdatum: 01.03.2011
Verlag: Springer-Verlag
Erschienen in: Structural and Multidisciplinary Optimization / Ausgabe 3/2011
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI: https://doi.org/10.1007/s00158-010-0560-4

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