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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

On the approximate reanalysis technique in topology optimization

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Senne, Thadeu A. [1] ; Gomes, Francisco A. M. [2] ; Santos, Sandra A. [2]
Total Authors: 3
[1] Univ Fed Sao Paulo, Inst Sci & Technol, Ave Cesare Mansueto Giulio Lattes 1201, BR-12247014 Sao Jose Dos Campos - Brazil
[2] Univ Estadual Campinas, Inst Math Stat & Sci Comp, Rua Sergio Buarque de Holanda 651, BR-13083859 Campinas, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: OPTIMIZATION AND ENGINEERING; v. 20, n. 1, p. 251-275, MAR 2019.
Web of Science Citations: 0

A classical problem in topology optimization concerns the minimization of the compliance of a static structure, subject to a volume constraint upon the available material. Assuming that the structure is under small displacements and it is composed of a linear elastic material, the evaluation of the objective function demands the solution of a linear system. Hence, within the computational optimization process of addressing topology optimization problems, the cost of evaluating the objective function may be an issue, especially as the discretized mesh is refined. This work pursues the approximate reanalysis technique in combination with the Sequential Piecewise Linear Programming method for obtaining optimized structures. Numerical evidences are presented to corroborate the usage of this blend in a study composed by three distinct strategies in three benchmark test problems. A further analysis has been performed concerning the impact of the computation of the gradient vector of the objective function, pointing out room for additional savings. (AU)

FAPESP's process: 13/05475-7 - Computational methods in optimization
Grantee:Sandra Augusta Santos
Support type: Research Projects - Thematic Grants
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC