Extracting the Solution of Three-Dimensional Wave Diffraction Problem from Two-Dimensional Analysis by Introducing an Artificial Neural Network for Floating Objects
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Latin American journal of solids and structures (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252020000800503 |
Resumo: | Abstract The diffraction of the waves from the two ends of floating breakwaters (FBWs) that have limited length, are practically a three-dimensional (3D). In order to perform a two-dimensional vertical (2DV) analysis to solve the wave diffraction problem, some “correcting factors” are required to modify the 2DV results and make them comparable and verifiable against 3D solutions. The main objective of the current study is to propose a method to obtain these correcting factors and demonstrate its usefulness through some example cases. An Artificial Neural Network (ANN) is trained by three main non-dimensional independent variables to predict the mentioned factors. In order to set up the ANN, a database including both 2DV and 3D results is required. The 2DV results are obtained by employing a semi-analytical method, namely the Scaled Boundary Finite Element Method (SBFEM). A basic change in the location of the scaling center is implemented. The 3D results are obtained via ANSYS AQWA software. Eighty-one cases are simulated on a floating object with rectangular cross-sections. The correlation factor R = 0.9607 for a group of new samples shows that the predicted results are closely matched to the target values. The correcting factor applies the 3D effects of diffracted waves around the structures on 2DV results and produces a more accurate prediction. |
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Latin American journal of solids and structures (Online) |
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Extracting the Solution of Three-Dimensional Wave Diffraction Problem from Two-Dimensional Analysis by Introducing an Artificial Neural Network for Floating ObjectsScaled Boundary Finite Element MethodWater Wave DiffractionArtificial Neural NetworkAbstract The diffraction of the waves from the two ends of floating breakwaters (FBWs) that have limited length, are practically a three-dimensional (3D). In order to perform a two-dimensional vertical (2DV) analysis to solve the wave diffraction problem, some “correcting factors” are required to modify the 2DV results and make them comparable and verifiable against 3D solutions. The main objective of the current study is to propose a method to obtain these correcting factors and demonstrate its usefulness through some example cases. An Artificial Neural Network (ANN) is trained by three main non-dimensional independent variables to predict the mentioned factors. In order to set up the ANN, a database including both 2DV and 3D results is required. The 2DV results are obtained by employing a semi-analytical method, namely the Scaled Boundary Finite Element Method (SBFEM). A basic change in the location of the scaling center is implemented. The 3D results are obtained via ANSYS AQWA software. Eighty-one cases are simulated on a floating object with rectangular cross-sections. The correlation factor R = 0.9607 for a group of new samples shows that the predicted results are closely matched to the target values. The correcting factor applies the 3D effects of diffracted waves around the structures on 2DV results and produces a more accurate prediction.Associação Brasileira de Ciências Mecânicas2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252020000800503Latin American Journal of Solids and Structures v.17 n.8 2020reponame:Latin American journal of solids and structures (Online)instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)instacron:ABCM10.1590/1679-78256096info:eu-repo/semantics/openAccessFouladi,Meisam QorbaniBadiei,PeymanVahdani,Shahrameng2020-11-19T00:00:00Zoai:scielo:S1679-78252020000800503Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=1679-7825&lng=pt&nrm=isohttps://old.scielo.br/oai/scielo-oai.phpabcm@abcm.org.br||maralves@usp.br1679-78251679-7817opendoar:2020-11-19T00:00Latin American journal of solids and structures (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)false |
dc.title.none.fl_str_mv |
Extracting the Solution of Three-Dimensional Wave Diffraction Problem from Two-Dimensional Analysis by Introducing an Artificial Neural Network for Floating Objects |
title |
Extracting the Solution of Three-Dimensional Wave Diffraction Problem from Two-Dimensional Analysis by Introducing an Artificial Neural Network for Floating Objects |
spellingShingle |
Extracting the Solution of Three-Dimensional Wave Diffraction Problem from Two-Dimensional Analysis by Introducing an Artificial Neural Network for Floating Objects Fouladi,Meisam Qorbani Scaled Boundary Finite Element Method Water Wave Diffraction Artificial Neural Network |
title_short |
Extracting the Solution of Three-Dimensional Wave Diffraction Problem from Two-Dimensional Analysis by Introducing an Artificial Neural Network for Floating Objects |
title_full |
Extracting the Solution of Three-Dimensional Wave Diffraction Problem from Two-Dimensional Analysis by Introducing an Artificial Neural Network for Floating Objects |
title_fullStr |
Extracting the Solution of Three-Dimensional Wave Diffraction Problem from Two-Dimensional Analysis by Introducing an Artificial Neural Network for Floating Objects |
title_full_unstemmed |
Extracting the Solution of Three-Dimensional Wave Diffraction Problem from Two-Dimensional Analysis by Introducing an Artificial Neural Network for Floating Objects |
title_sort |
Extracting the Solution of Three-Dimensional Wave Diffraction Problem from Two-Dimensional Analysis by Introducing an Artificial Neural Network for Floating Objects |
author |
Fouladi,Meisam Qorbani |
author_facet |
Fouladi,Meisam Qorbani Badiei,Peyman Vahdani,Shahram |
author_role |
author |
author2 |
Badiei,Peyman Vahdani,Shahram |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Fouladi,Meisam Qorbani Badiei,Peyman Vahdani,Shahram |
dc.subject.por.fl_str_mv |
Scaled Boundary Finite Element Method Water Wave Diffraction Artificial Neural Network |
topic |
Scaled Boundary Finite Element Method Water Wave Diffraction Artificial Neural Network |
description |
Abstract The diffraction of the waves from the two ends of floating breakwaters (FBWs) that have limited length, are practically a three-dimensional (3D). In order to perform a two-dimensional vertical (2DV) analysis to solve the wave diffraction problem, some “correcting factors” are required to modify the 2DV results and make them comparable and verifiable against 3D solutions. The main objective of the current study is to propose a method to obtain these correcting factors and demonstrate its usefulness through some example cases. An Artificial Neural Network (ANN) is trained by three main non-dimensional independent variables to predict the mentioned factors. In order to set up the ANN, a database including both 2DV and 3D results is required. The 2DV results are obtained by employing a semi-analytical method, namely the Scaled Boundary Finite Element Method (SBFEM). A basic change in the location of the scaling center is implemented. The 3D results are obtained via ANSYS AQWA software. Eighty-one cases are simulated on a floating object with rectangular cross-sections. The correlation factor R = 0.9607 for a group of new samples shows that the predicted results are closely matched to the target values. The correcting factor applies the 3D effects of diffracted waves around the structures on 2DV results and produces a more accurate prediction. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 |
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=S1679-78252020000800503 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252020000800503 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1679-78256096 |
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 Ciências Mecânicas |
publisher.none.fl_str_mv |
Associação Brasileira de Ciências Mecânicas |
dc.source.none.fl_str_mv |
Latin American Journal of Solids and Structures v.17 n.8 2020 reponame:Latin American journal of solids and structures (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 |
Latin American journal of solids and structures (Online) |
collection |
Latin American journal of solids and structures (Online) |
repository.name.fl_str_mv |
Latin American journal of solids and structures (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) |
repository.mail.fl_str_mv |
abcm@abcm.org.br||maralves@usp.br |
_version_ |
1754302890467393536 |