Bayesian model identification through harmonic balance method for hysteresis prediction in bolted joints
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s11071-021-06967-2 http://hdl.handle.net/11449/222871 |
Resumo: | Hysteresis is a nonlinear phenomenon present in many structures, such as those assembled by bolted joints. Despite a large number of recent findings related to identification techniques for these systems, the problem is still challenging and open to contributions. To fill the gap concerning the proposition of identification algorithms based on closed-form solutions, this work introduces the use of the harmonic balance method to identify a stochastic Bouc–Wen model for predicting the nonlinear behavior of bolted structures. A piecewise smooth procedure is applied on the hysteretic restoring force to become possible to derive an analytical approximation of the response based on the Fourier series. Firstly, the analytical approximation is used to calibrate deterministic Bouc–Wen parameters by minimizing the error between the Fourier amplitudes of the numerical model and those extracted from experimental data using the cross-entropy optimization method. Since the experimental data investigated here contain variability due to the measurement process (aleatoric uncertainties), the deterministic parameters are then used as a priori conditions to update their probability density functions via the Bayesian inference. Having the model parameters as random variables, the stochastic Bouc–Wen model is obtained. This methodology was illustrated in a bolted structure benchmark. The results indicate that the method proposed can identify an accurate stochastic Bouc–Wen model for predicting the dynamics of bolted structures, even taking into account data variability. |
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Repositório Institucional da UNESP |
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Bayesian model identification through harmonic balance method for hysteresis prediction in bolted jointsBayesian paradigmBolted jointsHarmonic balance methodHysteresisHysteresis is a nonlinear phenomenon present in many structures, such as those assembled by bolted joints. Despite a large number of recent findings related to identification techniques for these systems, the problem is still challenging and open to contributions. To fill the gap concerning the proposition of identification algorithms based on closed-form solutions, this work introduces the use of the harmonic balance method to identify a stochastic Bouc–Wen model for predicting the nonlinear behavior of bolted structures. A piecewise smooth procedure is applied on the hysteretic restoring force to become possible to derive an analytical approximation of the response based on the Fourier series. Firstly, the analytical approximation is used to calibrate deterministic Bouc–Wen parameters by minimizing the error between the Fourier amplitudes of the numerical model and those extracted from experimental data using the cross-entropy optimization method. Since the experimental data investigated here contain variability due to the measurement process (aleatoric uncertainties), the deterministic parameters are then used as a priori conditions to update their probability density functions via the Bayesian inference. Having the model parameters as random variables, the stochastic Bouc–Wen model is obtained. This methodology was illustrated in a bolted structure benchmark. The results indicate that the method proposed can identify an accurate stochastic Bouc–Wen model for predicting the dynamics of bolted structures, even taking into account data variability.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Department of Mechanical Engineering São Paulo State University (UNESP), Avenida Brasil 56, SPDepartment of Mechanical Engineering São Paulo State University (UNESP), Avenida Brasil 56, SPFAPESP: 2016/21973–5FAPESP: 2017/15512–8FAPESP: 2019/19684-3FAPESP: 2020/07449-7CNPq: 306526/2019-0Universidade Estadual Paulista (UNESP)Miguel, Luccas Pereira [UNESP]Teloli, Rafael de Oliveira [UNESP]Silva, Samuel da [UNESP]2022-04-28T19:47:13Z2022-04-28T19:47:13Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article77-98http://dx.doi.org/10.1007/s11071-021-06967-2Nonlinear Dynamics, v. 107, n. 1, p. 77-98, 2022.1573-269X0924-090Xhttp://hdl.handle.net/11449/22287110.1007/s11071-021-06967-22-s2.0-85119170557Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengNonlinear Dynamicsinfo:eu-repo/semantics/openAccess2022-04-28T19:47:13Zoai:repositorio.unesp.br:11449/222871Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:26:09.869274Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Bayesian model identification through harmonic balance method for hysteresis prediction in bolted joints |
title |
Bayesian model identification through harmonic balance method for hysteresis prediction in bolted joints |
spellingShingle |
Bayesian model identification through harmonic balance method for hysteresis prediction in bolted joints Miguel, Luccas Pereira [UNESP] Bayesian paradigm Bolted joints Harmonic balance method Hysteresis |
title_short |
Bayesian model identification through harmonic balance method for hysteresis prediction in bolted joints |
title_full |
Bayesian model identification through harmonic balance method for hysteresis prediction in bolted joints |
title_fullStr |
Bayesian model identification through harmonic balance method for hysteresis prediction in bolted joints |
title_full_unstemmed |
Bayesian model identification through harmonic balance method for hysteresis prediction in bolted joints |
title_sort |
Bayesian model identification through harmonic balance method for hysteresis prediction in bolted joints |
author |
Miguel, Luccas Pereira [UNESP] |
author_facet |
Miguel, Luccas Pereira [UNESP] Teloli, Rafael de Oliveira [UNESP] Silva, Samuel da [UNESP] |
author_role |
author |
author2 |
Teloli, Rafael de Oliveira [UNESP] Silva, Samuel da [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Miguel, Luccas Pereira [UNESP] Teloli, Rafael de Oliveira [UNESP] Silva, Samuel da [UNESP] |
dc.subject.por.fl_str_mv |
Bayesian paradigm Bolted joints Harmonic balance method Hysteresis |
topic |
Bayesian paradigm Bolted joints Harmonic balance method Hysteresis |
description |
Hysteresis is a nonlinear phenomenon present in many structures, such as those assembled by bolted joints. Despite a large number of recent findings related to identification techniques for these systems, the problem is still challenging and open to contributions. To fill the gap concerning the proposition of identification algorithms based on closed-form solutions, this work introduces the use of the harmonic balance method to identify a stochastic Bouc–Wen model for predicting the nonlinear behavior of bolted structures. A piecewise smooth procedure is applied on the hysteretic restoring force to become possible to derive an analytical approximation of the response based on the Fourier series. Firstly, the analytical approximation is used to calibrate deterministic Bouc–Wen parameters by minimizing the error between the Fourier amplitudes of the numerical model and those extracted from experimental data using the cross-entropy optimization method. Since the experimental data investigated here contain variability due to the measurement process (aleatoric uncertainties), the deterministic parameters are then used as a priori conditions to update their probability density functions via the Bayesian inference. Having the model parameters as random variables, the stochastic Bouc–Wen model is obtained. This methodology was illustrated in a bolted structure benchmark. The results indicate that the method proposed can identify an accurate stochastic Bouc–Wen model for predicting the dynamics of bolted structures, even taking into account data variability. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-28T19:47:13Z 2022-04-28T19:47:13Z 2022-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s11071-021-06967-2 Nonlinear Dynamics, v. 107, n. 1, p. 77-98, 2022. 1573-269X 0924-090X http://hdl.handle.net/11449/222871 10.1007/s11071-021-06967-2 2-s2.0-85119170557 |
url |
http://dx.doi.org/10.1007/s11071-021-06967-2 http://hdl.handle.net/11449/222871 |
identifier_str_mv |
Nonlinear Dynamics, v. 107, n. 1, p. 77-98, 2022. 1573-269X 0924-090X 10.1007/s11071-021-06967-2 2-s2.0-85119170557 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Nonlinear Dynamics |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
77-98 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128359539933184 |