Antonio Colanera, Johann Moritz Reumschüssel, Jan Paul Beuth, Matteo Chiatto, Luigi de Luca, Kilian Oberleithner
{"title":"湍流中相干结构的扩展聚类网络建模","authors":"Antonio Colanera, Johann Moritz Reumschüssel, Jan Paul Beuth, Matteo Chiatto, Luigi de Luca, Kilian Oberleithner","doi":"10.1007/s00162-024-00723-z","DOIUrl":null,"url":null,"abstract":"<div><p>This study introduces the Extended Cluster-based Network Modeling (eCNM), a methodology to analyze complex fluid flows. The eCNM focuses on characterizing dynamics within specific subspaces or subsets of variables, providing valuable insights into complex flow phenomena. The effectiveness of the eCNM is demonstrated on a swirl flame in unforced conditions, characterized by a precessing vortex core (PVC), using synchronized data from PIV measurements, UV-images filtered around the OH* chemiluminescence wavelength, featuring the heat release rate distribution, and pressure signals from jet inlet probes. The analysis starts with choosing the distance metric for the coarse-graining process and the number of clusters of the model. This has been pursued by designing a filtered distance metric based on the filtered correlation matrix and minimizing the Bayesian information criterion (BIC) score, balancing the goodness of the fit of a model with its complexity. The standard cluster-based network model on the velocity fluctuations allowed for determining the characteristic frequency of the PVC. The construction of extended cluster centroids of the heat release rate reveals a rotating flame pattern, predominantly localized within regions influenced by PVC’s vortices roll-up. Spatial subdomain analysis is carried out, demonstrating the benefits of focusing on specific regions of interest within the fluid system and providing significant computational savings. Furthermore, eCNM allows for the handling of different sampling frequencies among datasets. Leveraging high-resolution pressure measurements as a reference dataset and velocity components as undersampled data, extended cluster centroids for velocity are successfully estimated, even when the velocity sampling frequency is artificially reduced. This study showcases the adaptability and robustness of eCNM as a valuable tool for comprehending and analyzing coherent structures in complex fluid flows.\n</p></div>","PeriodicalId":795,"journal":{"name":"Theoretical and Computational Fluid Dynamics","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Extended cluster-based network modeling for coherent structures in turbulent flows\",\"authors\":\"Antonio Colanera, Johann Moritz Reumschüssel, Jan Paul Beuth, Matteo Chiatto, Luigi de Luca, Kilian Oberleithner\",\"doi\":\"10.1007/s00162-024-00723-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study introduces the Extended Cluster-based Network Modeling (eCNM), a methodology to analyze complex fluid flows. The eCNM focuses on characterizing dynamics within specific subspaces or subsets of variables, providing valuable insights into complex flow phenomena. The effectiveness of the eCNM is demonstrated on a swirl flame in unforced conditions, characterized by a precessing vortex core (PVC), using synchronized data from PIV measurements, UV-images filtered around the OH* chemiluminescence wavelength, featuring the heat release rate distribution, and pressure signals from jet inlet probes. The analysis starts with choosing the distance metric for the coarse-graining process and the number of clusters of the model. This has been pursued by designing a filtered distance metric based on the filtered correlation matrix and minimizing the Bayesian information criterion (BIC) score, balancing the goodness of the fit of a model with its complexity. The standard cluster-based network model on the velocity fluctuations allowed for determining the characteristic frequency of the PVC. The construction of extended cluster centroids of the heat release rate reveals a rotating flame pattern, predominantly localized within regions influenced by PVC’s vortices roll-up. Spatial subdomain analysis is carried out, demonstrating the benefits of focusing on specific regions of interest within the fluid system and providing significant computational savings. Furthermore, eCNM allows for the handling of different sampling frequencies among datasets. Leveraging high-resolution pressure measurements as a reference dataset and velocity components as undersampled data, extended cluster centroids for velocity are successfully estimated, even when the velocity sampling frequency is artificially reduced. This study showcases the adaptability and robustness of eCNM as a valuable tool for comprehending and analyzing coherent structures in complex fluid flows.\\n</p></div>\",\"PeriodicalId\":795,\"journal\":{\"name\":\"Theoretical and Computational Fluid Dynamics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Theoretical and Computational Fluid Dynamics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s00162-024-00723-z\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Theoretical and Computational Fluid Dynamics","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s00162-024-00723-z","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MECHANICS","Score":null,"Total":0}
Extended cluster-based network modeling for coherent structures in turbulent flows
This study introduces the Extended Cluster-based Network Modeling (eCNM), a methodology to analyze complex fluid flows. The eCNM focuses on characterizing dynamics within specific subspaces or subsets of variables, providing valuable insights into complex flow phenomena. The effectiveness of the eCNM is demonstrated on a swirl flame in unforced conditions, characterized by a precessing vortex core (PVC), using synchronized data from PIV measurements, UV-images filtered around the OH* chemiluminescence wavelength, featuring the heat release rate distribution, and pressure signals from jet inlet probes. The analysis starts with choosing the distance metric for the coarse-graining process and the number of clusters of the model. This has been pursued by designing a filtered distance metric based on the filtered correlation matrix and minimizing the Bayesian information criterion (BIC) score, balancing the goodness of the fit of a model with its complexity. The standard cluster-based network model on the velocity fluctuations allowed for determining the characteristic frequency of the PVC. The construction of extended cluster centroids of the heat release rate reveals a rotating flame pattern, predominantly localized within regions influenced by PVC’s vortices roll-up. Spatial subdomain analysis is carried out, demonstrating the benefits of focusing on specific regions of interest within the fluid system and providing significant computational savings. Furthermore, eCNM allows for the handling of different sampling frequencies among datasets. Leveraging high-resolution pressure measurements as a reference dataset and velocity components as undersampled data, extended cluster centroids for velocity are successfully estimated, even when the velocity sampling frequency is artificially reduced. This study showcases the adaptability and robustness of eCNM as a valuable tool for comprehending and analyzing coherent structures in complex fluid flows.
期刊介绍:
Theoretical and Computational Fluid Dynamics provides a forum for the cross fertilization of ideas, tools and techniques across all disciplines in which fluid flow plays a role. The focus is on aspects of fluid dynamics where theory and computation are used to provide insights and data upon which solid physical understanding is revealed. We seek research papers, invited review articles, brief communications, letters and comments addressing flow phenomena of relevance to aeronautical, geophysical, environmental, material, mechanical and life sciences. Papers of a purely algorithmic, experimental or engineering application nature, and papers without significant new physical insights, are outside the scope of this journal. For computational work, authors are responsible for ensuring that any artifacts of discretization and/or implementation are sufficiently controlled such that the numerical results unambiguously support the conclusions drawn. Where appropriate, and to the extent possible, such papers should either include or reference supporting documentation in the form of verification and validation studies.