Extreme vorticity events in turbulent Rayleigh-Bénard convection from stereoscopic measurements and reservoir computing
High-amplitude events of the out-of-plane vorticity component ω_{z} are analyzed by stereoscopic particle image velocimetry (PIV) in the bulk region of turbulent Rayleigh-Bénard convection in air. The Rayleigh numbers Ra vary from 1.7×10^{4} to 5.1×10^{5}. The experimental investigation is connected...
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| Format: | Article |
| Language: | English |
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American Physical Society
2022-06-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.4.023180 |
| _version_ | 1797210738979241984 |
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| author | Valentina Valori Robert Kräuter Jörg Schumacher |
| author_facet | Valentina Valori Robert Kräuter Jörg Schumacher |
| author_sort | Valentina Valori |
| collection | DOAJ |
| description | High-amplitude events of the out-of-plane vorticity component ω_{z} are analyzed by stereoscopic particle image velocimetry (PIV) in the bulk region of turbulent Rayleigh-Bénard convection in air. The Rayleigh numbers Ra vary from 1.7×10^{4} to 5.1×10^{5}. The experimental investigation is connected with a comprehensive statistical analysis of long-term time series of ω_{z} and individual velocity derivatives ∂u_{i}/∂x_{j}. A statistical convergence for derivative moments up to an order of 6 is demonstrated. Our results are found to agree well with existing high-resolution direct numerical simulation data in the same range of parameters, including the extreme vorticity events that appear in the far exponential tails of the corresponding probability density functions. The transition from Gaussian to non-Gaussian velocity derivative statistics in the bulk of a convection flow is confirmed experimentally. The experimental data are used to train a reservoir computing model, one implementation of a recurrent neural network, to reproduce highly intermittent experimental time series of the vorticity and thus reconstruct extreme out-of-plane vorticity events. After training the model with high-resolution PIV data, the machine learning model is run with sparsely seeded, continually available, and unseen measurement data in the reconstruction phase. The dependence of the reconstruction quality on the sparsity of the partial observations is also documented. Our latter result paves the way to machine-learning-assisted experimental analyses of small-scale turbulence for which time series of missing velocity derivatives can be provided by generative algorithms. |
| first_indexed | 2024-04-24T10:15:22Z |
| format | Article |
| id | doaj.art-9742cefe72334d0bad7f7393ddcdd56a |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:15:22Z |
| publishDate | 2022-06-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-9742cefe72334d0bad7f7393ddcdd56a2024-04-12T17:21:32ZengAmerican Physical SocietyPhysical Review Research2643-15642022-06-014202318010.1103/PhysRevResearch.4.023180Extreme vorticity events in turbulent Rayleigh-Bénard convection from stereoscopic measurements and reservoir computingValentina ValoriRobert KräuterJörg SchumacherHigh-amplitude events of the out-of-plane vorticity component ω_{z} are analyzed by stereoscopic particle image velocimetry (PIV) in the bulk region of turbulent Rayleigh-Bénard convection in air. The Rayleigh numbers Ra vary from 1.7×10^{4} to 5.1×10^{5}. The experimental investigation is connected with a comprehensive statistical analysis of long-term time series of ω_{z} and individual velocity derivatives ∂u_{i}/∂x_{j}. A statistical convergence for derivative moments up to an order of 6 is demonstrated. Our results are found to agree well with existing high-resolution direct numerical simulation data in the same range of parameters, including the extreme vorticity events that appear in the far exponential tails of the corresponding probability density functions. The transition from Gaussian to non-Gaussian velocity derivative statistics in the bulk of a convection flow is confirmed experimentally. The experimental data are used to train a reservoir computing model, one implementation of a recurrent neural network, to reproduce highly intermittent experimental time series of the vorticity and thus reconstruct extreme out-of-plane vorticity events. After training the model with high-resolution PIV data, the machine learning model is run with sparsely seeded, continually available, and unseen measurement data in the reconstruction phase. The dependence of the reconstruction quality on the sparsity of the partial observations is also documented. Our latter result paves the way to machine-learning-assisted experimental analyses of small-scale turbulence for which time series of missing velocity derivatives can be provided by generative algorithms.http://doi.org/10.1103/PhysRevResearch.4.023180 |
| spellingShingle | Valentina Valori Robert Kräuter Jörg Schumacher Extreme vorticity events in turbulent Rayleigh-Bénard convection from stereoscopic measurements and reservoir computing Physical Review Research |
| title | Extreme vorticity events in turbulent Rayleigh-Bénard convection from stereoscopic measurements and reservoir computing |
| title_full | Extreme vorticity events in turbulent Rayleigh-Bénard convection from stereoscopic measurements and reservoir computing |
| title_fullStr | Extreme vorticity events in turbulent Rayleigh-Bénard convection from stereoscopic measurements and reservoir computing |
| title_full_unstemmed | Extreme vorticity events in turbulent Rayleigh-Bénard convection from stereoscopic measurements and reservoir computing |
| title_short | Extreme vorticity events in turbulent Rayleigh-Bénard convection from stereoscopic measurements and reservoir computing |
| title_sort | extreme vorticity events in turbulent rayleigh benard convection from stereoscopic measurements and reservoir computing |
| url | http://doi.org/10.1103/PhysRevResearch.4.023180 |
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