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A Simple Matlab Code for Material Design Optimization Using Reduced Order Models

The main part of the computational cost required for solving the problem of optimal material design with extreme properties using a topology optimization formulation is devoted to solving the equilibrium system of equations derived through the implementation of the finite element method (FEM). To re...

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Detalles Bibliográficos
Autores principales: Kazakis, George, Lagaros, Nikos D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324003/
https://www.ncbi.nlm.nih.gov/pubmed/35888439
http://dx.doi.org/10.3390/ma15144972
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author Kazakis, George
Lagaros, Nikos D.
author_facet Kazakis, George
Lagaros, Nikos D.
author_sort Kazakis, George
collection PubMed
description The main part of the computational cost required for solving the problem of optimal material design with extreme properties using a topology optimization formulation is devoted to solving the equilibrium system of equations derived through the implementation of the finite element method (FEM). To reduce this computational cost, among other methodologies, various model order reduction (MOR) approaches can be utilized. In this work, a simple Matlab code for solving the topology optimization for the design of materials combined with three different model order reduction approaches is presented. The three MOR approaches presented in the code implementation are the proper orthogonal decomposition (POD), the on-the-fly reduced order model construction and the approximate reanalysis (AR) following the combined approximations approach. The complete code, containing all participating functions (including the changes made to the original ones), is provided.
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spelling pubmed-93240032022-07-27 A Simple Matlab Code for Material Design Optimization Using Reduced Order Models Kazakis, George Lagaros, Nikos D. Materials (Basel) Article The main part of the computational cost required for solving the problem of optimal material design with extreme properties using a topology optimization formulation is devoted to solving the equilibrium system of equations derived through the implementation of the finite element method (FEM). To reduce this computational cost, among other methodologies, various model order reduction (MOR) approaches can be utilized. In this work, a simple Matlab code for solving the topology optimization for the design of materials combined with three different model order reduction approaches is presented. The three MOR approaches presented in the code implementation are the proper orthogonal decomposition (POD), the on-the-fly reduced order model construction and the approximate reanalysis (AR) following the combined approximations approach. The complete code, containing all participating functions (including the changes made to the original ones), is provided. MDPI 2022-07-17 /pmc/articles/PMC9324003/ /pubmed/35888439 http://dx.doi.org/10.3390/ma15144972 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kazakis, George
Lagaros, Nikos D.
A Simple Matlab Code for Material Design Optimization Using Reduced Order Models
title A Simple Matlab Code for Material Design Optimization Using Reduced Order Models
title_full A Simple Matlab Code for Material Design Optimization Using Reduced Order Models
title_fullStr A Simple Matlab Code for Material Design Optimization Using Reduced Order Models
title_full_unstemmed A Simple Matlab Code for Material Design Optimization Using Reduced Order Models
title_short A Simple Matlab Code for Material Design Optimization Using Reduced Order Models
title_sort simple matlab code for material design optimization using reduced order models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324003/
https://www.ncbi.nlm.nih.gov/pubmed/35888439
http://dx.doi.org/10.3390/ma15144972
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