Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control

Autores
Parezanović, Vladimir; Laurentie, Jean Charles; Fourment, Carine; Delville, Joël; Bonnet, Jean-Paul; Spohn, Andreas; Duriez, Thomas Pierre Cornil; Cordier, Laurent; Noack, Bernd R.; Abel, Markus; Segond, Marc; Shaqarin, Tamir; Brunton, Steven L.
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Open- and closed-loop control of a turbulent mixing layer is experimentally performed in a dedicated large scale, low speed wind-tunnel facility. The flow is manipulated by an array of fluidic micro-valve actuators integrated into the trailing edge of a splitter plate. Sensing is performed using a rake of hot-wire probes downstream of the splitter plate in the mixing layer. The control goal is the manipulation of the local fluctuating energy level. The mixing layer's response to the control is tested with open-loop forcing with a wide range of actuation frequencies. Results are discussed for different closed-loop control approaches, such as: adaptive extremum-seeking and in-time POD mode feedback control. In addition, we propose Machine Learning Control (MLC) as a model-free closed-loop control method. MLC arrives reproducibly at the near-optimal in-time control.
Fil: Parezanović, Vladimir. Institut Pprime; Francia. Centre National de la Recherche Scientifique; Francia
Fil: Laurentie, Jean Charles. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia
Fil: Fourment, Carine. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia
Fil: Delville, Joël. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia
Fil: Bonnet, Jean-Paul. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia
Fil: Spohn, Andreas. Institut Pprime; Francia. Centre National de la Recherche Scientifique; Francia
Fil: Duriez, Thomas Pierre Cornil. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cordier, Laurent. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia
Fil: Noack, Bernd R.. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia
Fil: Abel, Markus. Ambrosys; Alemania. University of Potsdam; Alemania. Université de Lorraine; Francia
Fil: Segond, Marc. Ambrosys; Alemania. Taflia Technical University; Jordania
Fil: Shaqarin, Tamir. Taflia Technical University; Jordania
Fil: Brunton, Steven L.. University of Washington; Estados Unidos
Materia
Active Flow Control
Extremum Seeking
Genetic Programming
Machine Learning
Pod
Shear Flow
Turbulence
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/37726
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/37726
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning controlParezanović, VladimirLaurentie, Jean CharlesFourment, CarineDelville, JoëlBonnet, Jean-PaulSpohn, AndreasDuriez, Thomas Pierre CornilCordier, LaurentNoack, Bernd R.Abel, MarkusSegond, MarcShaqarin, TamirBrunton, Steven L.Active Flow ControlExtremum SeekingGenetic ProgrammingMachine LearningPodShear FlowTurbulencehttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Open- and closed-loop control of a turbulent mixing layer is experimentally performed in a dedicated large scale, low speed wind-tunnel facility. The flow is manipulated by an array of fluidic micro-valve actuators integrated into the trailing edge of a splitter plate. Sensing is performed using a rake of hot-wire probes downstream of the splitter plate in the mixing layer. The control goal is the manipulation of the local fluctuating energy level. The mixing layer's response to the control is tested with open-loop forcing with a wide range of actuation frequencies. Results are discussed for different closed-loop control approaches, such as: adaptive extremum-seeking and in-time POD mode feedback control. In addition, we propose Machine Learning Control (MLC) as a model-free closed-loop control method. MLC arrives reproducibly at the near-optimal in-time control.Fil: Parezanović, Vladimir. Institut Pprime; Francia. Centre National de la Recherche Scientifique; FranciaFil: Laurentie, Jean Charles. Centre National de la Recherche Scientifique; Francia. Institut Pprime; FranciaFil: Fourment, Carine. Centre National de la Recherche Scientifique; Francia. Institut Pprime; FranciaFil: Delville, Joël. Centre National de la Recherche Scientifique; Francia. Institut Pprime; FranciaFil: Bonnet, Jean-Paul. Centre National de la Recherche Scientifique; Francia. Institut Pprime; FranciaFil: Spohn, Andreas. Institut Pprime; Francia. Centre National de la Recherche Scientifique; FranciaFil: Duriez, Thomas Pierre Cornil. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cordier, Laurent. Centre National de la Recherche Scientifique; Francia. Institut Pprime; FranciaFil: Noack, Bernd R.. Centre National de la Recherche Scientifique; Francia. Institut Pprime; FranciaFil: Abel, Markus. Ambrosys; Alemania. University of Potsdam; Alemania. Université de Lorraine; FranciaFil: Segond, Marc. Ambrosys; Alemania. Taflia Technical University; JordaniaFil: Shaqarin, Tamir. Taflia Technical University; JordaniaFil: Brunton, Steven L.. University of Washington; Estados UnidosSpringer2015-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/37726Parezanović, Vladimir; Laurentie, Jean Charles; Fourment, Carine; Delville, Joël; Bonnet, Jean-Paul; et al.; Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control; Springer; Flow Turbulence And Combustion; 94; 1; 1-2015; 155-1731386-61841573-1987CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s10494-014-9581-1info:eu-repo/semantics/altIdentifier/doi/10.1007/s10494-014-9581-1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:26:51Zoai:ri.conicet.gov.ar:11336/37726instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:26:51.662CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control
title Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control
spellingShingle Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control
Parezanović, Vladimir
Active Flow Control
Extremum Seeking
Genetic Programming
Machine Learning
Pod
Shear Flow
Turbulence
title_short Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control
title_full Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control
title_fullStr Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control
title_full_unstemmed Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control
title_sort Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control
dc.creator.none.fl_str_mv Parezanović, Vladimir
Laurentie, Jean Charles
Fourment, Carine
Delville, Joël
Bonnet, Jean-Paul
Spohn, Andreas
Duriez, Thomas Pierre Cornil
Cordier, Laurent
Noack, Bernd R.
Abel, Markus
Segond, Marc
Shaqarin, Tamir
Brunton, Steven L.
author Parezanović, Vladimir
author_facet Parezanović, Vladimir
Laurentie, Jean Charles
Fourment, Carine
Delville, Joël
Bonnet, Jean-Paul
Spohn, Andreas
Duriez, Thomas Pierre Cornil
Cordier, Laurent
Noack, Bernd R.
Abel, Markus
Segond, Marc
Shaqarin, Tamir
Brunton, Steven L.
author_role author
author2 Laurentie, Jean Charles
Fourment, Carine
Delville, Joël
Bonnet, Jean-Paul
Spohn, Andreas
Duriez, Thomas Pierre Cornil
Cordier, Laurent
Noack, Bernd R.
Abel, Markus
Segond, Marc
Shaqarin, Tamir
Brunton, Steven L.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Active Flow Control
Extremum Seeking
Genetic Programming
Machine Learning
Pod
Shear Flow
Turbulence
topic Active Flow Control
Extremum Seeking
Genetic Programming
Machine Learning
Pod
Shear Flow
Turbulence
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Open- and closed-loop control of a turbulent mixing layer is experimentally performed in a dedicated large scale, low speed wind-tunnel facility. The flow is manipulated by an array of fluidic micro-valve actuators integrated into the trailing edge of a splitter plate. Sensing is performed using a rake of hot-wire probes downstream of the splitter plate in the mixing layer. The control goal is the manipulation of the local fluctuating energy level. The mixing layer's response to the control is tested with open-loop forcing with a wide range of actuation frequencies. Results are discussed for different closed-loop control approaches, such as: adaptive extremum-seeking and in-time POD mode feedback control. In addition, we propose Machine Learning Control (MLC) as a model-free closed-loop control method. MLC arrives reproducibly at the near-optimal in-time control.
Fil: Parezanović, Vladimir. Institut Pprime; Francia. Centre National de la Recherche Scientifique; Francia
Fil: Laurentie, Jean Charles. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia
Fil: Fourment, Carine. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia
Fil: Delville, Joël. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia
Fil: Bonnet, Jean-Paul. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia
Fil: Spohn, Andreas. Institut Pprime; Francia. Centre National de la Recherche Scientifique; Francia
Fil: Duriez, Thomas Pierre Cornil. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cordier, Laurent. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia
Fil: Noack, Bernd R.. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia
Fil: Abel, Markus. Ambrosys; Alemania. University of Potsdam; Alemania. Université de Lorraine; Francia
Fil: Segond, Marc. Ambrosys; Alemania. Taflia Technical University; Jordania
Fil: Shaqarin, Tamir. Taflia Technical University; Jordania
Fil: Brunton, Steven L.. University of Washington; Estados Unidos
description Open- and closed-loop control of a turbulent mixing layer is experimentally performed in a dedicated large scale, low speed wind-tunnel facility. The flow is manipulated by an array of fluidic micro-valve actuators integrated into the trailing edge of a splitter plate. Sensing is performed using a rake of hot-wire probes downstream of the splitter plate in the mixing layer. The control goal is the manipulation of the local fluctuating energy level. The mixing layer's response to the control is tested with open-loop forcing with a wide range of actuation frequencies. Results are discussed for different closed-loop control approaches, such as: adaptive extremum-seeking and in-time POD mode feedback control. In addition, we propose Machine Learning Control (MLC) as a model-free closed-loop control method. MLC arrives reproducibly at the near-optimal in-time control.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/37726
Parezanović, Vladimir; Laurentie, Jean Charles; Fourment, Carine; Delville, Joël; Bonnet, Jean-Paul; et al.; Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control; Springer; Flow Turbulence And Combustion; 94; 1; 1-2015; 155-173
1386-6184
1573-1987
CONICET Digital
CONICET
url http://hdl.handle.net/11336/37726
identifier_str_mv Parezanović, Vladimir; Laurentie, Jean Charles; Fourment, Carine; Delville, Joël; Bonnet, Jean-Paul; et al.; Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control; Springer; Flow Turbulence And Combustion; 94; 1; 1-2015; 155-173
1386-6184
1573-1987
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s10494-014-9581-1
info:eu-repo/semantics/altIdentifier/doi/10.1007/s10494-014-9581-1
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846082718554652672
score 12.891075

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