A specialized genetic algorithm for the electrical impedance tomography of two-phase flows
Autor(a) principal: | |
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Data de Publicação: | 2006 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1678-58782006000400002 |
Resumo: | The main objective of this work is to contribute to the development of a new tomographic reconstruction method well suited for processing signals obtained from electrical or other soft sensing field probes. The adopted approach consists in formulating the reconstruction problem in terms of an error function, which assesses the difference between a prospective and the actual internal contrast distribution (3D image), and searching for its minimum with the help of a specialized genetic algorithm (GA). Numerical simulations have been performed to demonstrate the feasibility of the proposed reconstruction method, as well as to emphasize the relation between the ill-posed nature of the problem and the topology of the minimization hyper-surface, and the importance of considering this relation when designing the numerical solution procedure. Results show that convergence is greatly enhanced when a priori information is introduced in the error function. |
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Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) |
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A specialized genetic algorithm for the electrical impedance tomography of two-phase flowsElectrical impedance tomographygenetic algorithminverse problemoptimizationmultiphase-flowThe main objective of this work is to contribute to the development of a new tomographic reconstruction method well suited for processing signals obtained from electrical or other soft sensing field probes. The adopted approach consists in formulating the reconstruction problem in terms of an error function, which assesses the difference between a prospective and the actual internal contrast distribution (3D image), and searching for its minimum with the help of a specialized genetic algorithm (GA). Numerical simulations have been performed to demonstrate the feasibility of the proposed reconstruction method, as well as to emphasize the relation between the ill-posed nature of the problem and the topology of the minimization hyper-surface, and the importance of considering this relation when designing the numerical solution procedure. Results show that convergence is greatly enhanced when a priori information is introduced in the error function.Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM2006-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1678-58782006000400002Journal of the Brazilian Society of Mechanical Sciences and Engineering v.28 n.4 2006reponame:Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online)instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)instacron:ABCM10.1590/S1678-58782006000400002info:eu-repo/semantics/openAccessRolnik,Vanessa P.Seleghim Jr.,Pauloeng2007-10-08T00:00:00Zoai:scielo:S1678-58782006000400002Revistahttps://www.scielo.br/j/jbsmse/https://old.scielo.br/oai/scielo-oai.php||abcm@abcm.org.br1806-36911678-5878opendoar:2007-10-08T00:00Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)false |
dc.title.none.fl_str_mv |
A specialized genetic algorithm for the electrical impedance tomography of two-phase flows |
title |
A specialized genetic algorithm for the electrical impedance tomography of two-phase flows |
spellingShingle |
A specialized genetic algorithm for the electrical impedance tomography of two-phase flows Rolnik,Vanessa P. Electrical impedance tomography genetic algorithm inverse problem optimization multiphase-flow |
title_short |
A specialized genetic algorithm for the electrical impedance tomography of two-phase flows |
title_full |
A specialized genetic algorithm for the electrical impedance tomography of two-phase flows |
title_fullStr |
A specialized genetic algorithm for the electrical impedance tomography of two-phase flows |
title_full_unstemmed |
A specialized genetic algorithm for the electrical impedance tomography of two-phase flows |
title_sort |
A specialized genetic algorithm for the electrical impedance tomography of two-phase flows |
author |
Rolnik,Vanessa P. |
author_facet |
Rolnik,Vanessa P. Seleghim Jr.,Paulo |
author_role |
author |
author2 |
Seleghim Jr.,Paulo |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Rolnik,Vanessa P. Seleghim Jr.,Paulo |
dc.subject.por.fl_str_mv |
Electrical impedance tomography genetic algorithm inverse problem optimization multiphase-flow |
topic |
Electrical impedance tomography genetic algorithm inverse problem optimization multiphase-flow |
description |
The main objective of this work is to contribute to the development of a new tomographic reconstruction method well suited for processing signals obtained from electrical or other soft sensing field probes. The adopted approach consists in formulating the reconstruction problem in terms of an error function, which assesses the difference between a prospective and the actual internal contrast distribution (3D image), and searching for its minimum with the help of a specialized genetic algorithm (GA). Numerical simulations have been performed to demonstrate the feasibility of the proposed reconstruction method, as well as to emphasize the relation between the ill-posed nature of the problem and the topology of the minimization hyper-surface, and the importance of considering this relation when designing the numerical solution procedure. Results show that convergence is greatly enhanced when a priori information is introduced in the error function. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006-12-01 |
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=S1678-58782006000400002 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1678-58782006000400002 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1678-58782006000400002 |
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 |
Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM |
dc.source.none.fl_str_mv |
Journal of the Brazilian Society of Mechanical Sciences and Engineering v.28 n.4 2006 reponame:Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) instacron:ABCM |
instname_str |
Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) |
instacron_str |
ABCM |
institution |
ABCM |
reponame_str |
Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) |
collection |
Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) |
repository.name.fl_str_mv |
Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) |
repository.mail.fl_str_mv |
||abcm@abcm.org.br |
_version_ |
1754734680890933248 |