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Structural and Multidisciplinary Optimization

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10.06.2021 | Educational Paper

TopADD: a 2D/3D integrated topology optimization parallel-computing framework for arbitrary design domains

verfasst von: Zhi-Dong Zhang, Osezua Ibhadode, Ali Bonakdar, Ehsan Toyserkani

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 3/2021

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Abstract

In this work, a two-dimensional (2D) and three-dimensional (3D) integrated topology optimization (TO) parallel-computing framework, named TopADD (TOPology optimization for Arbitrary Design Domains), is developed to deal with topology optimization problems with arbitrary design domains. The parallel-computing framework is an extended work of the initial parallel-computing framework developed by Aage et al. (Struct Multidiscip Optim 51(3): 565–572, 2015). The extension is threefold: (a) a 2D implementation has been incorporated into the framework to achieve seamless switching between 2D and 3D dimensions; (b) an efficient voxelizer that can initialize complex geometries into the design domains for topology optimization is developed; and (c) besides the compliance minimization problem, two other physics have been considered: the compliant mechanism and the heat conduction problems. Additionally, the computational efficiency of the proposed framework has been examined. Compared to the other frameworks in the literature, the proposed work has superior efficiency in both computational time and memory usage. Lastly, the proposed topology optimization framework’s compatibility with additive manufacturing (AM) has been demonstrated by exporting and printing the final optimized parts without postprocessing.

Vorheriger Artikel IgaTop: an implementation of topology optimization for structures using IGA in MATLAB

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Titel: TopADD: a 2D/3D integrated topology optimization parallel-computing framework for arbitrary design domains
verfasst von: Zhi-Dong Zhang
Osezua Ibhadode
Ali Bonakdar
Ehsan Toyserkani
Publikationsdatum: 10.06.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Structural and Multidisciplinary Optimization / Ausgabe 3/2021
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI: https://doi.org/10.1007/s00158-021-02917-z

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Weitere Artikel der Ausgabe 3/2021

Data-driven topology design using a deep generative model

Topology optimization of load-bearing capacity

Crashworthiness design of cross-sections with the Graph and Heuristic based Topology Optimization incorporating competing designs

A subinterval dimension-wise method for robust topology optimization of structures with truss-like lattice material under unknown but bounded uncertainties

A fast active learning method in design of experiments: multipeak parallel adaptive infilling strategy based on expected improvement

Design of adaptive structures through energy minimization: extension to tensegrity

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