A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function

Detalhes bibliográficos
Autor(a) principal: Babaei,Ali Reza
Data de Publicação: 2018
Outros Autores: Setayandeh,Mohammad Reza
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Journal of Aerospace Technology and Management (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2175-91462018000100330
Resumo: ABSTRACT: In this study, an efficient methodology is proposed for robust design optimization by using preference function and fuzzy logic concepts. In this method, the experience of experts is used as an important source of information during the design optimization process. The case study in this research is wing design optimization of Boeing 747. Optimization problem has two objective functions (wing weight and wing drag) so that they are transformed into new forms of objective functions based on fuzzy preference functions. Design constraints include transformation of fuel tank volume and lift coefficient into new constraints based on fuzzy preference function. The considered uncertainties are cruise velocity and altitude, which Monte Carlo simulation method is used for modeling them. The non-dominated sorting genetic algorithm is used as the optimization algorithm that can generate set of solutions as Pareto frontier. Ultimate distance concept is used for selecting the best solution among Pareto frontier. The results of the probabilistic analysis show that the obtained configuration is less sensitive to uncertainties.
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spelling A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference FunctionRobust designOptimizationFuzzy logicPreference functionsABSTRACT: In this study, an efficient methodology is proposed for robust design optimization by using preference function and fuzzy logic concepts. In this method, the experience of experts is used as an important source of information during the design optimization process. The case study in this research is wing design optimization of Boeing 747. Optimization problem has two objective functions (wing weight and wing drag) so that they are transformed into new forms of objective functions based on fuzzy preference functions. Design constraints include transformation of fuel tank volume and lift coefficient into new constraints based on fuzzy preference function. The considered uncertainties are cruise velocity and altitude, which Monte Carlo simulation method is used for modeling them. The non-dominated sorting genetic algorithm is used as the optimization algorithm that can generate set of solutions as Pareto frontier. Ultimate distance concept is used for selecting the best solution among Pareto frontier. The results of the probabilistic analysis show that the obtained configuration is less sensitive to uncertainties.Departamento de Ciência e Tecnologia Aeroespacial2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2175-91462018000100330Journal of Aerospace Technology and Management v.10 2018reponame:Journal of Aerospace Technology and Management (Online)instname:Departamento de Ciência e Tecnologia Aeroespacial (DCTA)instacron:DCTA10.5028/jatm.v10.934info:eu-repo/semantics/openAccessBabaei,Ali RezaSetayandeh,Mohammad Rezaeng2018-07-17T00:00:00Zoai:scielo:S2175-91462018000100330Revistahttp://www.jatm.com.br/ONGhttps://old.scielo.br/oai/scielo-oai.php||secretary@jatm.com.br2175-91461984-9648opendoar:2018-07-17T00:00Journal of Aerospace Technology and Management (Online) - Departamento de Ciência e Tecnologia Aeroespacial (DCTA)false
dc.title.none.fl_str_mv A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function
title A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function
spellingShingle A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function
Babaei,Ali Reza
Robust design
Optimization
Fuzzy logic
Preference functions
title_short A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function
title_full A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function
title_fullStr A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function
title_full_unstemmed A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function
title_sort A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function
author Babaei,Ali Reza
author_facet Babaei,Ali Reza
Setayandeh,Mohammad Reza
author_role author
author2 Setayandeh,Mohammad Reza
author2_role author
dc.contributor.author.fl_str_mv Babaei,Ali Reza
Setayandeh,Mohammad Reza
dc.subject.por.fl_str_mv Robust design
Optimization
Fuzzy logic
Preference functions
topic Robust design
Optimization
Fuzzy logic
Preference functions
description ABSTRACT: In this study, an efficient methodology is proposed for robust design optimization by using preference function and fuzzy logic concepts. In this method, the experience of experts is used as an important source of information during the design optimization process. The case study in this research is wing design optimization of Boeing 747. Optimization problem has two objective functions (wing weight and wing drag) so that they are transformed into new forms of objective functions based on fuzzy preference functions. Design constraints include transformation of fuel tank volume and lift coefficient into new constraints based on fuzzy preference function. The considered uncertainties are cruise velocity and altitude, which Monte Carlo simulation method is used for modeling them. The non-dominated sorting genetic algorithm is used as the optimization algorithm that can generate set of solutions as Pareto frontier. Ultimate distance concept is used for selecting the best solution among Pareto frontier. The results of the probabilistic analysis show that the obtained configuration is less sensitive to uncertainties.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2175-91462018000100330
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2175-91462018000100330
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.5028/jatm.v10.934
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Departamento de Ciência e Tecnologia Aeroespacial
publisher.none.fl_str_mv Departamento de Ciência e Tecnologia Aeroespacial
dc.source.none.fl_str_mv Journal of Aerospace Technology and Management v.10 2018
reponame:Journal of Aerospace Technology and Management (Online)
instname:Departamento de Ciência e Tecnologia Aeroespacial (DCTA)
instacron:DCTA
instname_str Departamento de Ciência e Tecnologia Aeroespacial (DCTA)
instacron_str DCTA
institution DCTA
reponame_str Journal of Aerospace Technology and Management (Online)
collection Journal of Aerospace Technology and Management (Online)
repository.name.fl_str_mv Journal of Aerospace Technology and Management (Online) - Departamento de Ciência e Tecnologia Aeroespacial (DCTA)
repository.mail.fl_str_mv ||secretary@jatm.com.br
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