Inferring wall-bounded coherent structures from two-dimensional turbulent fields via SHAP analysis

[EN] The analysis of coherent structures in turbulent flows is essential for understanding and controlling turbulence. Explainable Deep Learning (XDL) has been successfully used to identify the most important flow structures in turbulent channels (Cremades et al., 2024), but its computational cost g...

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Autores: Molina-Casino, Samuel, Cremades, Andrés, Cardesa, Jose I., Chedevergne, François, Vinuesa, Ricardo, Hoyas, Sergio|||0000-0002-8458-7288
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/222739
Acceso en línea:https://riunet.upv.es/handle/10251/222739
Access Level:acceso abierto
Palabra clave:Turbulence
Explainable deep learning
Shapley values
Coherent structures
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
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spelling Inferring wall-bounded coherent structures from two-dimensional turbulent fields via SHAP analysisMolina-Casino, SamuelCremades, AndrésCardesa, Jose I.Chedevergne, FrançoisVinuesa, RicardoHoyas, Sergio|||0000-0002-8458-7288TurbulenceExplainable deep learningShapley valuesCoherent structures09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación[EN] The analysis of coherent structures in turbulent flows is essential for understanding and controlling turbulence. Explainable Deep Learning (XDL) has been successfully used to identify the most important flow structures in turbulent channels (Cremades et al., 2024), but its computational cost grows significantly as Reynolds number increases. This study adapts the XDL framework to assess whether observations from time-resolved volumetric data of direct numerical simulations (DNS) can be inferred using two-dimensional (2D) slices. Limiting the analysis to 2D subsets reduces the cost of three-dimensional (3D) post-processing and facilitates its application to planar particle image velocimetry (PIV) experimental data. We employ SHapley Additive exPlanations (SHAP), a game-theoretic method, to identify dynamically relevant flow regions by assigning importance scores. Using DNS data from a turbulent channel flow at a friction Reynolds number of 125, we compare SHAP-based structures derived from 3D and 2D data with classically studied structures, including Q events, streaks, and vortex clusters. Our findings show that SHAP-based structures from 2D data statistically agree with those from full 3D fields and previously reported in the literature. These results underscore the potential of SHAP analyses for studying coherent structures with 2D data, enabling experimental and DNS research at higher Reynolds number.The data has been obtained with support of grant PID2021-128676OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe", by the European Union (SH) . RV acknowledges the financial support from ERC grant no. 2021-CoG-101043998, DEEPCONTROL. Views and opinions expressed are however those of the author (s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.ElsevierDepartamento de Máquinas y Motores TérmicosEscuela Técnica Superior de Ingeniería Aeroespacial y Diseño IndustrialInstituto Universitario de Matemática Pura y AplicadaEuropean CommissionAgencia Estatal de InvestigaciónEuropean Regional Development FundRepositorio Institucional de la Universitat Politècnica de València Riunet20252025-12-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/222739reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-128676OB-I00 REVELANDO LA TURBULENCIA DE PAREDEuropean Commission https://doi.org/10.13039/501100000780 HE 101043998 Discovering novel control strategies for turbulent wings through deep reinforcement learningopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2227392026-06-13T07:49:27Z
dc.title.none.fl_str_mv Inferring wall-bounded coherent structures from two-dimensional turbulent fields via SHAP analysis
title Inferring wall-bounded coherent structures from two-dimensional turbulent fields via SHAP analysis
spellingShingle Inferring wall-bounded coherent structures from two-dimensional turbulent fields via SHAP analysis
Molina-Casino, Samuel
Turbulence
Explainable deep learning
Shapley values
Coherent structures
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
title_short Inferring wall-bounded coherent structures from two-dimensional turbulent fields via SHAP analysis
title_full Inferring wall-bounded coherent structures from two-dimensional turbulent fields via SHAP analysis
title_fullStr Inferring wall-bounded coherent structures from two-dimensional turbulent fields via SHAP analysis
title_full_unstemmed Inferring wall-bounded coherent structures from two-dimensional turbulent fields via SHAP analysis
title_sort Inferring wall-bounded coherent structures from two-dimensional turbulent fields via SHAP analysis
dc.creator.none.fl_str_mv Molina-Casino, Samuel
Cremades, Andrés
Cardesa, Jose I.
Chedevergne, François
Vinuesa, Ricardo
Hoyas, Sergio|||0000-0002-8458-7288
author Molina-Casino, Samuel
author_facet Molina-Casino, Samuel
Cremades, Andrés
Cardesa, Jose I.
Chedevergne, François
Vinuesa, Ricardo
Hoyas, Sergio|||0000-0002-8458-7288
author_role author
author2 Cremades, Andrés
Cardesa, Jose I.
Chedevergne, François
Vinuesa, Ricardo
Hoyas, Sergio|||0000-0002-8458-7288
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Máquinas y Motores Térmicos
Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial
Instituto Universitario de Matemática Pura y Aplicada
European Commission
Agencia Estatal de Investigación
European Regional Development Fund
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Turbulence
Explainable deep learning
Shapley values
Coherent structures
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
topic Turbulence
Explainable deep learning
Shapley values
Coherent structures
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
description [EN] The analysis of coherent structures in turbulent flows is essential for understanding and controlling turbulence. Explainable Deep Learning (XDL) has been successfully used to identify the most important flow structures in turbulent channels (Cremades et al., 2024), but its computational cost grows significantly as Reynolds number increases. This study adapts the XDL framework to assess whether observations from time-resolved volumetric data of direct numerical simulations (DNS) can be inferred using two-dimensional (2D) slices. Limiting the analysis to 2D subsets reduces the cost of three-dimensional (3D) post-processing and facilitates its application to planar particle image velocimetry (PIV) experimental data. We employ SHapley Additive exPlanations (SHAP), a game-theoretic method, to identify dynamically relevant flow regions by assigning importance scores. Using DNS data from a turbulent channel flow at a friction Reynolds number of 125, we compare SHAP-based structures derived from 3D and 2D data with classically studied structures, including Q events, streaks, and vortex clusters. Our findings show that SHAP-based structures from 2D data statistically agree with those from full 3D fields and previously reported in the literature. These results underscore the potential of SHAP analyses for studying coherent structures with 2D data, enabling experimental and DNS research at higher Reynolds number.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-12-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/222739
url https://riunet.upv.es/handle/10251/222739
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-128676OB-I00 REVELANDO LA TURBULENCIA DE PARED
European Commission https://doi.org/10.13039/501100000780 HE 101043998 Discovering novel control strategies for turbulent wings through deep reinforcement learning
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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