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A FE 2 multi-scale implementation for modeling composite materials on distributed architectures

Giuntoli, Gustavo et al · Techno-Press · 2019

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This work investigates the accuracy and performance of a FE2 multi-scale implementation used to predict the behavior of composite materials. The equations are formulated assuming the small deformations solid mechanics approach in non-linear material models with hardening plasticity. The uniform strain boundary conditions are applied for the macro-to-micro transitions. A parallel algorithm was implemented in order to solve large engineering problems. The scheme proposed takes advantage of the domain decomposition method at the macro-scale and the coupling between each subdomain with a micro-scale model. The precision of the method is validated with a composite material problem and scalability tests are performed for showing the efficiency Fil: Giuntoli, Gustavo. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; España Fil: Aguilar, Jimmy. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; España

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APA 7

Giuntoli, G. E. A. (2019). A FE 2 multi-scale implementation for modeling composite materials on distributed architectures. http://hdl.handle.net/11336/120486

MLA

Giuntoli, Gustavo et al. "A FE 2 multi-scale implementation for modeling composite materials on distributed architectures." 2019. http://hdl.handle.net/11336/120486.

Chicago

Giuntoli, Gustavo et al. 2019. "A FE 2 multi-scale implementation for modeling composite materials on distributed architectures.". http://hdl.handle.net/11336/120486.

Harvard

Giuntoli, G. E. A. 2019, A FE 2 multi-scale implementation for modeling composite materials on distributed architectures, Techno-Press, available at: http://hdl.handle.net/11336/120486 [Accessed 9 Aug. 2026].

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Title
A FE 2 multi-scale implementation for modeling composite materials on distributed architectures
Author / contributors
Giuntoli, Gustavo et al
Publisher
Techno-Press
Publication year
2019
ISSN
2234-2184
ISSN
2234-2184
Language
English

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