Optimizing the control of transition to turbulence using a Bayesian method
The nonlinear robustness of laminar plane Couette flow is considered under the action of in-phase spanwise wall oscillations by computing properties of the edge of chaos, i.e. the boundary of its basins of attraction. Three measures are used to quantify the chosen control strategy on laminar-to-turb...
Gespeichert in:
Veröffentlicht in: | Journal of fluid mechanics 2022-06, Vol.941, Article A25 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | Journal of fluid mechanics |
container_volume | 941 |
creator | Pershin, Anton Beaume, Cédric Eaves, Tom S. Tobias, Steven M. |
description | The nonlinear robustness of laminar plane Couette flow is considered under the action of in-phase spanwise wall oscillations by computing properties of the edge of chaos, i.e. the boundary of its basins of attraction. Three measures are used to quantify the chosen control strategy on laminar-to-turbulent transition: the kinetic energy of edge states (local attractors on the edge of chaos), the form of the minimal seed (least energetic perturbation on the edge of chaos), and the laminarization probability (the probability that a random perturbation from the laminar flow of given kinetic energy will laminarize). A novel Bayesian approach is introduced to enable the accurate computation of the laminarization probability at a fraction of the cost of previous methods. While the edge state and the minimal seed provide useful information about the dynamics of transition to turbulence, neither measure is particularly useful to judge the effectiveness of the control strategy since they are not representative of the global geometry of the edge. In contrast, the laminarization probability provides global information about the edge and can be used to evaluate the control effectiveness by computing a laminarization score (the expected laminarization probability) and the associated expected dissipation rate of the controlled flow. These two quantities allow for the determination of optimal control parameter values subject to desired constraints. The results discussed in the paper are expected to be applied to a wide range of transitional flows and control strategies aimed at suppressing or triggering transition to turbulence. |
doi_str_mv | 10.1017/jfm.2022.298 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2655522236</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><cupid>10_1017_jfm_2022_298</cupid><sourcerecordid>2655522236</sourcerecordid><originalsourceid>FETCH-LOGICAL-c302t-21e86e7829bcd94e12de0e4af31e31338e2beb24d643aeb5e3bac8afe4b14b4a3</originalsourceid><addsrcrecordid>eNptkEtLAzEUhYMoWKs7f0DArTMmN5nXUotPCt3oOiQzd9qUzmRMMov6653SghtXBy7fPQc-Qm45SznjxcO27VJgAClU5RmZcZlXSZHL7JzM2HROOAd2Sa5C2DLGBauKGflYDdF29sf2axo3SGvXR-921LU0et0HG63raXQ0jt6MO-xrpGM40Jo-6T0Gq3vaYdy45ppctHoX8OaUc_L18vy5eEuWq9f3xeMyqQWDmADHMseihMrUTSWRQ4MMpW4FR8GFKBEMGpBNLoVGk6Ewui51i9JwaaQWc3J37B28-x4xRLV1o--nSQV5lmUAIPKJuj9StXcheGzV4G2n_V5xpg621GRLHWypydaEpydcd8bbZo1_rf8-_AIe3W1m</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2655522236</pqid></control><display><type>article</type><title>Optimizing the control of transition to turbulence using a Bayesian method</title><source>Cambridge University Press Journals Complete</source><creator>Pershin, Anton ; Beaume, Cédric ; Eaves, Tom S. ; Tobias, Steven M.</creator><creatorcontrib>Pershin, Anton ; Beaume, Cédric ; Eaves, Tom S. ; Tobias, Steven M.</creatorcontrib><description>The nonlinear robustness of laminar plane Couette flow is considered under the action of in-phase spanwise wall oscillations by computing properties of the edge of chaos, i.e. the boundary of its basins of attraction. Three measures are used to quantify the chosen control strategy on laminar-to-turbulent transition: the kinetic energy of edge states (local attractors on the edge of chaos), the form of the minimal seed (least energetic perturbation on the edge of chaos), and the laminarization probability (the probability that a random perturbation from the laminar flow of given kinetic energy will laminarize). A novel Bayesian approach is introduced to enable the accurate computation of the laminarization probability at a fraction of the cost of previous methods. While the edge state and the minimal seed provide useful information about the dynamics of transition to turbulence, neither measure is particularly useful to judge the effectiveness of the control strategy since they are not representative of the global geometry of the edge. In contrast, the laminarization probability provides global information about the edge and can be used to evaluate the control effectiveness by computing a laminarization score (the expected laminarization probability) and the associated expected dissipation rate of the controlled flow. These two quantities allow for the determination of optimal control parameter values subject to desired constraints. The results discussed in the paper are expected to be applied to a wide range of transitional flows and control strategies aimed at suppressing or triggering transition to turbulence.</description><identifier>ISSN: 0022-1120</identifier><identifier>EISSN: 1469-7645</identifier><identifier>DOI: 10.1017/jfm.2022.298</identifier><language>eng</language><publisher>Cambridge, UK: Cambridge University Press</publisher><subject>Bayesian analysis ; Bayesian theory ; Chaos ; Computation ; Control ; Couette flow ; Energy ; Global geometry ; JFM Papers ; Kinetic energy ; Laminar flow ; Optimal control ; Optimization ; Oscillations ; Perturbation ; Probability ; Probability theory ; Reynolds number ; Seeds ; Turbulence ; Viscosity</subject><ispartof>Journal of fluid mechanics, 2022-06, Vol.941, Article A25</ispartof><rights>The Author(s), 2022. Published by Cambridge University Press</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c302t-21e86e7829bcd94e12de0e4af31e31338e2beb24d643aeb5e3bac8afe4b14b4a3</citedby><cites>FETCH-LOGICAL-c302t-21e86e7829bcd94e12de0e4af31e31338e2beb24d643aeb5e3bac8afe4b14b4a3</cites><orcidid>0000-0003-3485-6387 ; 0000-0003-3473-1306 ; 0000-0003-2108-1906 ; 0000-0003-0205-7716</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.cambridge.org/core/product/identifier/S0022112022002981/type/journal_article$$EHTML$$P50$$Gcambridge$$H</linktohtml><link.rule.ids>164,314,776,780,27903,27904,55607</link.rule.ids></links><search><creatorcontrib>Pershin, Anton</creatorcontrib><creatorcontrib>Beaume, Cédric</creatorcontrib><creatorcontrib>Eaves, Tom S.</creatorcontrib><creatorcontrib>Tobias, Steven M.</creatorcontrib><title>Optimizing the control of transition to turbulence using a Bayesian method</title><title>Journal of fluid mechanics</title><addtitle>J. Fluid Mech</addtitle><description>The nonlinear robustness of laminar plane Couette flow is considered under the action of in-phase spanwise wall oscillations by computing properties of the edge of chaos, i.e. the boundary of its basins of attraction. Three measures are used to quantify the chosen control strategy on laminar-to-turbulent transition: the kinetic energy of edge states (local attractors on the edge of chaos), the form of the minimal seed (least energetic perturbation on the edge of chaos), and the laminarization probability (the probability that a random perturbation from the laminar flow of given kinetic energy will laminarize). A novel Bayesian approach is introduced to enable the accurate computation of the laminarization probability at a fraction of the cost of previous methods. While the edge state and the minimal seed provide useful information about the dynamics of transition to turbulence, neither measure is particularly useful to judge the effectiveness of the control strategy since they are not representative of the global geometry of the edge. In contrast, the laminarization probability provides global information about the edge and can be used to evaluate the control effectiveness by computing a laminarization score (the expected laminarization probability) and the associated expected dissipation rate of the controlled flow. These two quantities allow for the determination of optimal control parameter values subject to desired constraints. The results discussed in the paper are expected to be applied to a wide range of transitional flows and control strategies aimed at suppressing or triggering transition to turbulence.</description><subject>Bayesian analysis</subject><subject>Bayesian theory</subject><subject>Chaos</subject><subject>Computation</subject><subject>Control</subject><subject>Couette flow</subject><subject>Energy</subject><subject>Global geometry</subject><subject>JFM Papers</subject><subject>Kinetic energy</subject><subject>Laminar flow</subject><subject>Optimal control</subject><subject>Optimization</subject><subject>Oscillations</subject><subject>Perturbation</subject><subject>Probability</subject><subject>Probability theory</subject><subject>Reynolds number</subject><subject>Seeds</subject><subject>Turbulence</subject><subject>Viscosity</subject><issn>0022-1120</issn><issn>1469-7645</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNptkEtLAzEUhYMoWKs7f0DArTMmN5nXUotPCt3oOiQzd9qUzmRMMov6653SghtXBy7fPQc-Qm45SznjxcO27VJgAClU5RmZcZlXSZHL7JzM2HROOAd2Sa5C2DLGBauKGflYDdF29sf2axo3SGvXR-921LU0et0HG63raXQ0jt6MO-xrpGM40Jo-6T0Gq3vaYdy45ppctHoX8OaUc_L18vy5eEuWq9f3xeMyqQWDmADHMseihMrUTSWRQ4MMpW4FR8GFKBEMGpBNLoVGk6Ewui51i9JwaaQWc3J37B28-x4xRLV1o--nSQV5lmUAIPKJuj9StXcheGzV4G2n_V5xpg621GRLHWypydaEpydcd8bbZo1_rf8-_AIe3W1m</recordid><startdate>20220625</startdate><enddate>20220625</enddate><creator>Pershin, Anton</creator><creator>Beaume, Cédric</creator><creator>Eaves, Tom S.</creator><creator>Tobias, Steven M.</creator><general>Cambridge University Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TB</scope><scope>7U5</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H8D</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L.G</scope><scope>L6V</scope><scope>L7M</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>S0W</scope><orcidid>https://orcid.org/0000-0003-3485-6387</orcidid><orcidid>https://orcid.org/0000-0003-3473-1306</orcidid><orcidid>https://orcid.org/0000-0003-2108-1906</orcidid><orcidid>https://orcid.org/0000-0003-0205-7716</orcidid></search><sort><creationdate>20220625</creationdate><title>Optimizing the control of transition to turbulence using a Bayesian method</title><author>Pershin, Anton ; Beaume, Cédric ; Eaves, Tom S. ; Tobias, Steven M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c302t-21e86e7829bcd94e12de0e4af31e31338e2beb24d643aeb5e3bac8afe4b14b4a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Bayesian analysis</topic><topic>Bayesian theory</topic><topic>Chaos</topic><topic>Computation</topic><topic>Control</topic><topic>Couette flow</topic><topic>Energy</topic><topic>Global geometry</topic><topic>JFM Papers</topic><topic>Kinetic energy</topic><topic>Laminar flow</topic><topic>Optimal control</topic><topic>Optimization</topic><topic>Oscillations</topic><topic>Perturbation</topic><topic>Probability</topic><topic>Probability theory</topic><topic>Reynolds number</topic><topic>Seeds</topic><topic>Turbulence</topic><topic>Viscosity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pershin, Anton</creatorcontrib><creatorcontrib>Beaume, Cédric</creatorcontrib><creatorcontrib>Eaves, Tom S.</creatorcontrib><creatorcontrib>Tobias, Steven M.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><jtitle>Journal of fluid mechanics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pershin, Anton</au><au>Beaume, Cédric</au><au>Eaves, Tom S.</au><au>Tobias, Steven M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimizing the control of transition to turbulence using a Bayesian method</atitle><jtitle>Journal of fluid mechanics</jtitle><addtitle>J. Fluid Mech</addtitle><date>2022-06-25</date><risdate>2022</risdate><volume>941</volume><artnum>A25</artnum><issn>0022-1120</issn><eissn>1469-7645</eissn><abstract>The nonlinear robustness of laminar plane Couette flow is considered under the action of in-phase spanwise wall oscillations by computing properties of the edge of chaos, i.e. the boundary of its basins of attraction. Three measures are used to quantify the chosen control strategy on laminar-to-turbulent transition: the kinetic energy of edge states (local attractors on the edge of chaos), the form of the minimal seed (least energetic perturbation on the edge of chaos), and the laminarization probability (the probability that a random perturbation from the laminar flow of given kinetic energy will laminarize). A novel Bayesian approach is introduced to enable the accurate computation of the laminarization probability at a fraction of the cost of previous methods. While the edge state and the minimal seed provide useful information about the dynamics of transition to turbulence, neither measure is particularly useful to judge the effectiveness of the control strategy since they are not representative of the global geometry of the edge. In contrast, the laminarization probability provides global information about the edge and can be used to evaluate the control effectiveness by computing a laminarization score (the expected laminarization probability) and the associated expected dissipation rate of the controlled flow. These two quantities allow for the determination of optimal control parameter values subject to desired constraints. The results discussed in the paper are expected to be applied to a wide range of transitional flows and control strategies aimed at suppressing or triggering transition to turbulence.</abstract><cop>Cambridge, UK</cop><pub>Cambridge University Press</pub><doi>10.1017/jfm.2022.298</doi><tpages>31</tpages><orcidid>https://orcid.org/0000-0003-3485-6387</orcidid><orcidid>https://orcid.org/0000-0003-3473-1306</orcidid><orcidid>https://orcid.org/0000-0003-2108-1906</orcidid><orcidid>https://orcid.org/0000-0003-0205-7716</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0022-1120 |
ispartof | Journal of fluid mechanics, 2022-06, Vol.941, Article A25 |
issn | 0022-1120 1469-7645 |
language | eng |
recordid | cdi_proquest_journals_2655522236 |
source | Cambridge University Press Journals Complete |
subjects | Bayesian analysis Bayesian theory Chaos Computation Control Couette flow Energy Global geometry JFM Papers Kinetic energy Laminar flow Optimal control Optimization Oscillations Perturbation Probability Probability theory Reynolds number Seeds Turbulence Viscosity |
title | Optimizing the control of transition to turbulence using a Bayesian method |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T12%3A55%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Optimizing%20the%20control%20of%20transition%20to%20turbulence%20using%20a%20Bayesian%20method&rft.jtitle=Journal%20of%20fluid%20mechanics&rft.au=Pershin,%20Anton&rft.date=2022-06-25&rft.volume=941&rft.artnum=A25&rft.issn=0022-1120&rft.eissn=1469-7645&rft_id=info:doi/10.1017/jfm.2022.298&rft_dat=%3Cproquest_cross%3E2655522236%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2655522236&rft_id=info:pmid/&rft_cupid=10_1017_jfm_2022_298&rfr_iscdi=true |