Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data
Recent work has demonstrated the use of sparse sensors in combination with the proper orthogonal decomposition (POD) to produce data-driven reconstructions of the full velocity fields in a variety of flows. The present work investigates the fidelity of such techniques applied to a stalled NACA 0012...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2021-01-01
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| Series: | Data-Centric Engineering |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673621000058/type/journal_article |
| _version_ | 1811156484600365056 |
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| author | Douglas W. Carter Francis De Voogt Renan Soares Bharathram Ganapathisubramani |
| author_facet | Douglas W. Carter Francis De Voogt Renan Soares Bharathram Ganapathisubramani |
| author_sort | Douglas W. Carter |
| collection | DOAJ |
| description | Recent work has demonstrated the use of sparse sensors in combination with the proper orthogonal decomposition (POD) to produce data-driven reconstructions of the full velocity fields in a variety of flows. The present work investigates the fidelity of such techniques applied to a stalled NACA 0012 aerofoil at $ {Re}_c=75,000 $ at an angle of attack $ \alpha ={12}^{\circ } $ as measured experimentally using planar time-resolved particle image velocimetry. In contrast to many previous studies, the flow is absent of any dominant shedding frequency and exhibits a broad range of singular values due to the turbulence in the separated region. Several reconstruction methodologies for linear state estimation based on classical compressed sensing and extended POD methodologies are presented as well as nonlinear refinement through the use of a shallow neural network (SNN). It is found that the linear reconstructions inspired by the extended POD are inferior to the compressed sensing approach provided that the sparse sensors avoid regions of the flow with small variance across the global POD basis. Regardless of the linear method used, the nonlinear SNN gives strikingly similar performance in its refinement of the reconstructions. The capability of sparse sensors to reconstruct separated turbulent flow measurements is further discussed and directions for future work suggested. |
| first_indexed | 2024-04-10T04:51:21Z |
| format | Article |
| id | doaj.art-4387dfa40c394d0f867402c020dfb47f |
| institution | Directory Open Access Journal |
| issn | 2632-6736 |
| language | English |
| last_indexed | 2024-04-10T04:51:21Z |
| publishDate | 2021-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data-Centric Engineering |
| spelling | doaj.art-4387dfa40c394d0f867402c020dfb47f2023-03-09T12:31:48ZengCambridge University PressData-Centric Engineering2632-67362021-01-01210.1017/dce.2021.5Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental dataDouglas W. Carter0https://orcid.org/0000-0001-8675-1849Francis De Voogt1Renan Soares2Bharathram Ganapathisubramani3Department of Aeronautical & Astronautical Engineering, University of Southampton, Southampton SO17 1BJ, United KingdomDepartment of Aeronautical & Astronautical Engineering, University of Southampton, Southampton SO17 1BJ, United KingdomDepartment of Aeronautical & Astronautical Engineering, University of Southampton, Southampton SO17 1BJ, United KingdomDepartment of Aeronautical & Astronautical Engineering, University of Southampton, Southampton SO17 1BJ, United KingdomRecent work has demonstrated the use of sparse sensors in combination with the proper orthogonal decomposition (POD) to produce data-driven reconstructions of the full velocity fields in a variety of flows. The present work investigates the fidelity of such techniques applied to a stalled NACA 0012 aerofoil at $ {Re}_c=75,000 $ at an angle of attack $ \alpha ={12}^{\circ } $ as measured experimentally using planar time-resolved particle image velocimetry. In contrast to many previous studies, the flow is absent of any dominant shedding frequency and exhibits a broad range of singular values due to the turbulence in the separated region. Several reconstruction methodologies for linear state estimation based on classical compressed sensing and extended POD methodologies are presented as well as nonlinear refinement through the use of a shallow neural network (SNN). It is found that the linear reconstructions inspired by the extended POD are inferior to the compressed sensing approach provided that the sparse sensors avoid regions of the flow with small variance across the global POD basis. Regardless of the linear method used, the nonlinear SNN gives strikingly similar performance in its refinement of the reconstructions. The capability of sparse sensors to reconstruct separated turbulent flow measurements is further discussed and directions for future work suggested.https://www.cambridge.org/core/product/identifier/S2632673621000058/type/journal_articleExtended proper orthogonal decompositionseparated aerofoilshallow neural networksparse reconstruction |
| spellingShingle | Douglas W. Carter Francis De Voogt Renan Soares Bharathram Ganapathisubramani Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data Data-Centric Engineering Extended proper orthogonal decomposition separated aerofoil shallow neural network sparse reconstruction |
| title | Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data |
| title_full | Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data |
| title_fullStr | Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data |
| title_full_unstemmed | Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data |
| title_short | Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data |
| title_sort | data driven sparse reconstruction of flow over a stalled aerofoil using experimental data |
| topic | Extended proper orthogonal decomposition separated aerofoil shallow neural network sparse reconstruction |
| url | https://www.cambridge.org/core/product/identifier/S2632673621000058/type/journal_article |
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