Towards robust data-driven reduced-order modelling for turbulent flows: application to vortex-induced vibrations
Abstract This work presents a robust method that minimises the impact of user-selected parameter on the identification of generic models to study the coherent dynamics in turbulent flows. The objective is to gain insight into the flow dynamics from a data-driven reduced order model (ROM) that is dev...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Schubert, Yannick [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Theoretical and computational fluid dynamics - Springer Berlin Heidelberg, 1989, 36(2022), 3 vom: 23. Mai, Seite 517-543 |
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| Übergeordnetes Werk: |
volume:36 ; year:2022 ; number:3 ; day:23 ; month:05 ; pages:517-543 |
| Links: |
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| DOI / URN: |
10.1007/s00162-022-00609-y |
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| Katalog-ID: |
OLC2078961191 |
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| 520 | |a Abstract This work presents a robust method that minimises the impact of user-selected parameter on the identification of generic models to study the coherent dynamics in turbulent flows. The objective is to gain insight into the flow dynamics from a data-driven reduced order model (ROM) that is developed from measurement data of the respective flow. For an efficient separation of the coherent dynamics, spectral proper orthogonal decomposition (SPOD) is used, projecting the flow field onto a low-dimensional subspace, so that the dominating dynamics can be represented with a minimal number of modes. A function library is defined using polynomial combinations of the temporal modal coefficients to describe the flow dynamics with a system of nonlinear ordinary differential equations. The most important library functions are identified in a two-stage cross-validation procedure (conservative and restrictive sparsification) and combined in the final model. In the first stage, the process uses a simple approximation of the derivative to match the model with the data. This stage delivers a reduced set of possible library function candidates for the model. In the second, more complex stage, the model of the entire flow is integrated over a short time and compared with the progression of the measured data. This restrictive stage allows a robust identification of nonlinearities and modal interactions in the data and their representation in the model. The method is demonstrated using data from particle image velocimetry (PIV) measurements of a circular cylinder undergoing vortex-induced vibration (VIV) at $$\mathrm{Re}=4000$$. It delivers a reduced order model that reproduces the average dynamics of the flow and reveals the interaction of coexisting flow dynamics by the model structure. | ||
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| 650 | 4 | |a Vortex-induced vibration | |
| 650 | 4 | |a Circular cylinder wakes | |
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10.1007/s00162-022-00609-y doi (DE-627)OLC2078961191 (DE-He213)s00162-022-00609-y-p DE-627 ger DE-627 rakwb eng 530 620 VZ 510 530 VZ Schubert, Yannick verfasserin (orcid)0000-0002-8583-3583 aut Towards robust data-driven reduced-order modelling for turbulent flows: application to vortex-induced vibrations 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract This work presents a robust method that minimises the impact of user-selected parameter on the identification of generic models to study the coherent dynamics in turbulent flows. The objective is to gain insight into the flow dynamics from a data-driven reduced order model (ROM) that is developed from measurement data of the respective flow. For an efficient separation of the coherent dynamics, spectral proper orthogonal decomposition (SPOD) is used, projecting the flow field onto a low-dimensional subspace, so that the dominating dynamics can be represented with a minimal number of modes. A function library is defined using polynomial combinations of the temporal modal coefficients to describe the flow dynamics with a system of nonlinear ordinary differential equations. The most important library functions are identified in a two-stage cross-validation procedure (conservative and restrictive sparsification) and combined in the final model. In the first stage, the process uses a simple approximation of the derivative to match the model with the data. This stage delivers a reduced set of possible library function candidates for the model. In the second, more complex stage, the model of the entire flow is integrated over a short time and compared with the progression of the measured data. This restrictive stage allows a robust identification of nonlinearities and modal interactions in the data and their representation in the model. The method is demonstrated using data from particle image velocimetry (PIV) measurements of a circular cylinder undergoing vortex-induced vibration (VIV) at $$\mathrm{Re}=4000$$. It delivers a reduced order model that reproduces the average dynamics of the flow and reveals the interaction of coexisting flow dynamics by the model structure. Reduced-order model Vortex-induced vibration Circular cylinder wakes Spectral proper orthogonal decomposition Nonlinear ODE system Sparse systems Sieber, Moritz (orcid)0000-0002-8150-4977 aut Oberleithner, Kilian (orcid)0000-0003-0964-872X aut Martinuzzi, Robert (orcid)0000-0003-4349-2896 aut Enthalten in Theoretical and computational fluid dynamics Springer Berlin Heidelberg, 1989 36(2022), 3 vom: 23. Mai, Seite 517-543 (DE-627)130799521 (DE-600)1007949-X (DE-576)023042370 0935-4964 nnns volume:36 year:2022 number:3 day:23 month:05 pages:517-543 https://doi.org/10.1007/s00162-022-00609-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_20 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 36 2022 3 23 05 517-543 |
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10.1007/s00162-022-00609-y doi (DE-627)OLC2078961191 (DE-He213)s00162-022-00609-y-p DE-627 ger DE-627 rakwb eng 530 620 VZ 510 530 VZ Schubert, Yannick verfasserin (orcid)0000-0002-8583-3583 aut Towards robust data-driven reduced-order modelling for turbulent flows: application to vortex-induced vibrations 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract This work presents a robust method that minimises the impact of user-selected parameter on the identification of generic models to study the coherent dynamics in turbulent flows. The objective is to gain insight into the flow dynamics from a data-driven reduced order model (ROM) that is developed from measurement data of the respective flow. For an efficient separation of the coherent dynamics, spectral proper orthogonal decomposition (SPOD) is used, projecting the flow field onto a low-dimensional subspace, so that the dominating dynamics can be represented with a minimal number of modes. A function library is defined using polynomial combinations of the temporal modal coefficients to describe the flow dynamics with a system of nonlinear ordinary differential equations. The most important library functions are identified in a two-stage cross-validation procedure (conservative and restrictive sparsification) and combined in the final model. In the first stage, the process uses a simple approximation of the derivative to match the model with the data. This stage delivers a reduced set of possible library function candidates for the model. In the second, more complex stage, the model of the entire flow is integrated over a short time and compared with the progression of the measured data. This restrictive stage allows a robust identification of nonlinearities and modal interactions in the data and their representation in the model. The method is demonstrated using data from particle image velocimetry (PIV) measurements of a circular cylinder undergoing vortex-induced vibration (VIV) at $$\mathrm{Re}=4000$$. It delivers a reduced order model that reproduces the average dynamics of the flow and reveals the interaction of coexisting flow dynamics by the model structure. Reduced-order model Vortex-induced vibration Circular cylinder wakes Spectral proper orthogonal decomposition Nonlinear ODE system Sparse systems Sieber, Moritz (orcid)0000-0002-8150-4977 aut Oberleithner, Kilian (orcid)0000-0003-0964-872X aut Martinuzzi, Robert (orcid)0000-0003-4349-2896 aut Enthalten in Theoretical and computational fluid dynamics Springer Berlin Heidelberg, 1989 36(2022), 3 vom: 23. Mai, Seite 517-543 (DE-627)130799521 (DE-600)1007949-X (DE-576)023042370 0935-4964 nnns volume:36 year:2022 number:3 day:23 month:05 pages:517-543 https://doi.org/10.1007/s00162-022-00609-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_20 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 36 2022 3 23 05 517-543 |
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10.1007/s00162-022-00609-y doi (DE-627)OLC2078961191 (DE-He213)s00162-022-00609-y-p DE-627 ger DE-627 rakwb eng 530 620 VZ 510 530 VZ Schubert, Yannick verfasserin (orcid)0000-0002-8583-3583 aut Towards robust data-driven reduced-order modelling for turbulent flows: application to vortex-induced vibrations 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract This work presents a robust method that minimises the impact of user-selected parameter on the identification of generic models to study the coherent dynamics in turbulent flows. The objective is to gain insight into the flow dynamics from a data-driven reduced order model (ROM) that is developed from measurement data of the respective flow. For an efficient separation of the coherent dynamics, spectral proper orthogonal decomposition (SPOD) is used, projecting the flow field onto a low-dimensional subspace, so that the dominating dynamics can be represented with a minimal number of modes. A function library is defined using polynomial combinations of the temporal modal coefficients to describe the flow dynamics with a system of nonlinear ordinary differential equations. The most important library functions are identified in a two-stage cross-validation procedure (conservative and restrictive sparsification) and combined in the final model. In the first stage, the process uses a simple approximation of the derivative to match the model with the data. This stage delivers a reduced set of possible library function candidates for the model. In the second, more complex stage, the model of the entire flow is integrated over a short time and compared with the progression of the measured data. This restrictive stage allows a robust identification of nonlinearities and modal interactions in the data and their representation in the model. The method is demonstrated using data from particle image velocimetry (PIV) measurements of a circular cylinder undergoing vortex-induced vibration (VIV) at $$\mathrm{Re}=4000$$. It delivers a reduced order model that reproduces the average dynamics of the flow and reveals the interaction of coexisting flow dynamics by the model structure. Reduced-order model Vortex-induced vibration Circular cylinder wakes Spectral proper orthogonal decomposition Nonlinear ODE system Sparse systems Sieber, Moritz (orcid)0000-0002-8150-4977 aut Oberleithner, Kilian (orcid)0000-0003-0964-872X aut Martinuzzi, Robert (orcid)0000-0003-4349-2896 aut Enthalten in Theoretical and computational fluid dynamics Springer Berlin Heidelberg, 1989 36(2022), 3 vom: 23. Mai, Seite 517-543 (DE-627)130799521 (DE-600)1007949-X (DE-576)023042370 0935-4964 nnns volume:36 year:2022 number:3 day:23 month:05 pages:517-543 https://doi.org/10.1007/s00162-022-00609-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_20 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 36 2022 3 23 05 517-543 |
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10.1007/s00162-022-00609-y doi (DE-627)OLC2078961191 (DE-He213)s00162-022-00609-y-p DE-627 ger DE-627 rakwb eng 530 620 VZ 510 530 VZ Schubert, Yannick verfasserin (orcid)0000-0002-8583-3583 aut Towards robust data-driven reduced-order modelling for turbulent flows: application to vortex-induced vibrations 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract This work presents a robust method that minimises the impact of user-selected parameter on the identification of generic models to study the coherent dynamics in turbulent flows. The objective is to gain insight into the flow dynamics from a data-driven reduced order model (ROM) that is developed from measurement data of the respective flow. For an efficient separation of the coherent dynamics, spectral proper orthogonal decomposition (SPOD) is used, projecting the flow field onto a low-dimensional subspace, so that the dominating dynamics can be represented with a minimal number of modes. A function library is defined using polynomial combinations of the temporal modal coefficients to describe the flow dynamics with a system of nonlinear ordinary differential equations. The most important library functions are identified in a two-stage cross-validation procedure (conservative and restrictive sparsification) and combined in the final model. In the first stage, the process uses a simple approximation of the derivative to match the model with the data. This stage delivers a reduced set of possible library function candidates for the model. In the second, more complex stage, the model of the entire flow is integrated over a short time and compared with the progression of the measured data. This restrictive stage allows a robust identification of nonlinearities and modal interactions in the data and their representation in the model. The method is demonstrated using data from particle image velocimetry (PIV) measurements of a circular cylinder undergoing vortex-induced vibration (VIV) at $$\mathrm{Re}=4000$$. It delivers a reduced order model that reproduces the average dynamics of the flow and reveals the interaction of coexisting flow dynamics by the model structure. Reduced-order model Vortex-induced vibration Circular cylinder wakes Spectral proper orthogonal decomposition Nonlinear ODE system Sparse systems Sieber, Moritz (orcid)0000-0002-8150-4977 aut Oberleithner, Kilian (orcid)0000-0003-0964-872X aut Martinuzzi, Robert (orcid)0000-0003-4349-2896 aut Enthalten in Theoretical and computational fluid dynamics Springer Berlin Heidelberg, 1989 36(2022), 3 vom: 23. Mai, Seite 517-543 (DE-627)130799521 (DE-600)1007949-X (DE-576)023042370 0935-4964 nnns volume:36 year:2022 number:3 day:23 month:05 pages:517-543 https://doi.org/10.1007/s00162-022-00609-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_20 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 36 2022 3 23 05 517-543 |
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10.1007/s00162-022-00609-y doi (DE-627)OLC2078961191 (DE-He213)s00162-022-00609-y-p DE-627 ger DE-627 rakwb eng 530 620 VZ 510 530 VZ Schubert, Yannick verfasserin (orcid)0000-0002-8583-3583 aut Towards robust data-driven reduced-order modelling for turbulent flows: application to vortex-induced vibrations 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract This work presents a robust method that minimises the impact of user-selected parameter on the identification of generic models to study the coherent dynamics in turbulent flows. The objective is to gain insight into the flow dynamics from a data-driven reduced order model (ROM) that is developed from measurement data of the respective flow. For an efficient separation of the coherent dynamics, spectral proper orthogonal decomposition (SPOD) is used, projecting the flow field onto a low-dimensional subspace, so that the dominating dynamics can be represented with a minimal number of modes. A function library is defined using polynomial combinations of the temporal modal coefficients to describe the flow dynamics with a system of nonlinear ordinary differential equations. The most important library functions are identified in a two-stage cross-validation procedure (conservative and restrictive sparsification) and combined in the final model. In the first stage, the process uses a simple approximation of the derivative to match the model with the data. This stage delivers a reduced set of possible library function candidates for the model. In the second, more complex stage, the model of the entire flow is integrated over a short time and compared with the progression of the measured data. This restrictive stage allows a robust identification of nonlinearities and modal interactions in the data and their representation in the model. The method is demonstrated using data from particle image velocimetry (PIV) measurements of a circular cylinder undergoing vortex-induced vibration (VIV) at $$\mathrm{Re}=4000$$. It delivers a reduced order model that reproduces the average dynamics of the flow and reveals the interaction of coexisting flow dynamics by the model structure. Reduced-order model Vortex-induced vibration Circular cylinder wakes Spectral proper orthogonal decomposition Nonlinear ODE system Sparse systems Sieber, Moritz (orcid)0000-0002-8150-4977 aut Oberleithner, Kilian (orcid)0000-0003-0964-872X aut Martinuzzi, Robert (orcid)0000-0003-4349-2896 aut Enthalten in Theoretical and computational fluid dynamics Springer Berlin Heidelberg, 1989 36(2022), 3 vom: 23. Mai, Seite 517-543 (DE-627)130799521 (DE-600)1007949-X (DE-576)023042370 0935-4964 nnns volume:36 year:2022 number:3 day:23 month:05 pages:517-543 https://doi.org/10.1007/s00162-022-00609-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_20 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 36 2022 3 23 05 517-543 |
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Enthalten in Theoretical and computational fluid dynamics 36(2022), 3 vom: 23. Mai, Seite 517-543 volume:36 year:2022 number:3 day:23 month:05 pages:517-543 |
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Enthalten in Theoretical and computational fluid dynamics 36(2022), 3 vom: 23. Mai, Seite 517-543 volume:36 year:2022 number:3 day:23 month:05 pages:517-543 |
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Schubert, Yannick @@aut@@ Sieber, Moritz @@aut@@ Oberleithner, Kilian @@aut@@ Martinuzzi, Robert @@aut@@ |
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towards robust data-driven reduced-order modelling for turbulent flows: application to vortex-induced vibrations |
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Towards robust data-driven reduced-order modelling for turbulent flows: application to vortex-induced vibrations |
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Abstract This work presents a robust method that minimises the impact of user-selected parameter on the identification of generic models to study the coherent dynamics in turbulent flows. The objective is to gain insight into the flow dynamics from a data-driven reduced order model (ROM) that is developed from measurement data of the respective flow. For an efficient separation of the coherent dynamics, spectral proper orthogonal decomposition (SPOD) is used, projecting the flow field onto a low-dimensional subspace, so that the dominating dynamics can be represented with a minimal number of modes. A function library is defined using polynomial combinations of the temporal modal coefficients to describe the flow dynamics with a system of nonlinear ordinary differential equations. The most important library functions are identified in a two-stage cross-validation procedure (conservative and restrictive sparsification) and combined in the final model. In the first stage, the process uses a simple approximation of the derivative to match the model with the data. This stage delivers a reduced set of possible library function candidates for the model. In the second, more complex stage, the model of the entire flow is integrated over a short time and compared with the progression of the measured data. This restrictive stage allows a robust identification of nonlinearities and modal interactions in the data and their representation in the model. The method is demonstrated using data from particle image velocimetry (PIV) measurements of a circular cylinder undergoing vortex-induced vibration (VIV) at $$\mathrm{Re}=4000$$. It delivers a reduced order model that reproduces the average dynamics of the flow and reveals the interaction of coexisting flow dynamics by the model structure. © The Author(s) 2022 |
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Abstract This work presents a robust method that minimises the impact of user-selected parameter on the identification of generic models to study the coherent dynamics in turbulent flows. The objective is to gain insight into the flow dynamics from a data-driven reduced order model (ROM) that is developed from measurement data of the respective flow. For an efficient separation of the coherent dynamics, spectral proper orthogonal decomposition (SPOD) is used, projecting the flow field onto a low-dimensional subspace, so that the dominating dynamics can be represented with a minimal number of modes. A function library is defined using polynomial combinations of the temporal modal coefficients to describe the flow dynamics with a system of nonlinear ordinary differential equations. The most important library functions are identified in a two-stage cross-validation procedure (conservative and restrictive sparsification) and combined in the final model. In the first stage, the process uses a simple approximation of the derivative to match the model with the data. This stage delivers a reduced set of possible library function candidates for the model. In the second, more complex stage, the model of the entire flow is integrated over a short time and compared with the progression of the measured data. This restrictive stage allows a robust identification of nonlinearities and modal interactions in the data and their representation in the model. The method is demonstrated using data from particle image velocimetry (PIV) measurements of a circular cylinder undergoing vortex-induced vibration (VIV) at $$\mathrm{Re}=4000$$. It delivers a reduced order model that reproduces the average dynamics of the flow and reveals the interaction of coexisting flow dynamics by the model structure. © The Author(s) 2022 |
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Abstract This work presents a robust method that minimises the impact of user-selected parameter on the identification of generic models to study the coherent dynamics in turbulent flows. The objective is to gain insight into the flow dynamics from a data-driven reduced order model (ROM) that is developed from measurement data of the respective flow. For an efficient separation of the coherent dynamics, spectral proper orthogonal decomposition (SPOD) is used, projecting the flow field onto a low-dimensional subspace, so that the dominating dynamics can be represented with a minimal number of modes. A function library is defined using polynomial combinations of the temporal modal coefficients to describe the flow dynamics with a system of nonlinear ordinary differential equations. The most important library functions are identified in a two-stage cross-validation procedure (conservative and restrictive sparsification) and combined in the final model. In the first stage, the process uses a simple approximation of the derivative to match the model with the data. This stage delivers a reduced set of possible library function candidates for the model. In the second, more complex stage, the model of the entire flow is integrated over a short time and compared with the progression of the measured data. This restrictive stage allows a robust identification of nonlinearities and modal interactions in the data and their representation in the model. The method is demonstrated using data from particle image velocimetry (PIV) measurements of a circular cylinder undergoing vortex-induced vibration (VIV) at $$\mathrm{Re}=4000$$. It delivers a reduced order model that reproduces the average dynamics of the flow and reveals the interaction of coexisting flow dynamics by the model structure. © The Author(s) 2022 |
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