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Reliability based geometrically nonlinear bi-directional evolutionary structural optimization of elasto-plastic material

The aim of this paper is to integrate the reliability-based analysis into topology optimization problems. Consequently, reliability-based topology optimization (RBTO) of geometrically nonlinear elasto-plastic models is presented. For purpose of performing (RBTO), the volume fraction is considered re...

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Autores principales: Habashneh, Muayad, Movahedi Rad, Majid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994787/
https://www.ncbi.nlm.nih.gov/pubmed/35397628
http://dx.doi.org/10.1038/s41598-022-09612-z
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author Habashneh, Muayad
Movahedi Rad, Majid
author_facet Habashneh, Muayad
Movahedi Rad, Majid
author_sort Habashneh, Muayad
collection PubMed
description The aim of this paper is to integrate the reliability-based analysis into topology optimization problems. Consequently, reliability-based topology optimization (RBTO) of geometrically nonlinear elasto-plastic models is presented. For purpose of performing (RBTO), the volume fraction is considered reliable since that the application of (RBTO) gives different topology in comparison to the deterministic topology optimization. The effects of changing the prescribed total structural volume constraint for deterministic designs and changing the reliability index for probabilistic designs are considered. Reliability index works as a constraint which is related to reliability condition added into the volume fraction and it is calculated using the Monte-Carlo simulation approach in the case of probabilistic design. In addition, bi-directional evolutionary structural optimization (BESO) method is utilized to study the effect of geometrically nonlinear elasto-plastic design. The plastic behavior can be controlled by defining a limit on the plastic limit load multipliers. The suggested work's efficiency is demonstrated via a 2D benchmark problem. In case of elastic material, a 2D model of U-shape plate is used for probabilistic design of linear and geometrically nonlinear topology optimizations. Furthermore, a 2D elasto-plastic model is considered for reliability-based design to demonstrate that the suggested approach can determine the best topological solution.
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spelling pubmed-89947872022-04-13 Reliability based geometrically nonlinear bi-directional evolutionary structural optimization of elasto-plastic material Habashneh, Muayad Movahedi Rad, Majid Sci Rep Article The aim of this paper is to integrate the reliability-based analysis into topology optimization problems. Consequently, reliability-based topology optimization (RBTO) of geometrically nonlinear elasto-plastic models is presented. For purpose of performing (RBTO), the volume fraction is considered reliable since that the application of (RBTO) gives different topology in comparison to the deterministic topology optimization. The effects of changing the prescribed total structural volume constraint for deterministic designs and changing the reliability index for probabilistic designs are considered. Reliability index works as a constraint which is related to reliability condition added into the volume fraction and it is calculated using the Monte-Carlo simulation approach in the case of probabilistic design. In addition, bi-directional evolutionary structural optimization (BESO) method is utilized to study the effect of geometrically nonlinear elasto-plastic design. The plastic behavior can be controlled by defining a limit on the plastic limit load multipliers. The suggested work's efficiency is demonstrated via a 2D benchmark problem. In case of elastic material, a 2D model of U-shape plate is used for probabilistic design of linear and geometrically nonlinear topology optimizations. Furthermore, a 2D elasto-plastic model is considered for reliability-based design to demonstrate that the suggested approach can determine the best topological solution. Nature Publishing Group UK 2022-04-09 /pmc/articles/PMC8994787/ /pubmed/35397628 http://dx.doi.org/10.1038/s41598-022-09612-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Habashneh, Muayad
Movahedi Rad, Majid
Reliability based geometrically nonlinear bi-directional evolutionary structural optimization of elasto-plastic material
title Reliability based geometrically nonlinear bi-directional evolutionary structural optimization of elasto-plastic material
title_full Reliability based geometrically nonlinear bi-directional evolutionary structural optimization of elasto-plastic material
title_fullStr Reliability based geometrically nonlinear bi-directional evolutionary structural optimization of elasto-plastic material
title_full_unstemmed Reliability based geometrically nonlinear bi-directional evolutionary structural optimization of elasto-plastic material
title_short Reliability based geometrically nonlinear bi-directional evolutionary structural optimization of elasto-plastic material
title_sort reliability based geometrically nonlinear bi-directional evolutionary structural optimization of elasto-plastic material
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994787/
https://www.ncbi.nlm.nih.gov/pubmed/35397628
http://dx.doi.org/10.1038/s41598-022-09612-z
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