Generation of turbulent inflow data from realistic approximations of the covariance tensor
This study presents a novel synthetic inflow generator capable of producing a random field matching a realistic set of two-point statistics with minimal input. The method is based on two main elements. The first element is a procedure to infer realistic two-point covariance tensors from readily avai...
| Päätekijät: | , , |
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| Aineistotyyppi: | Journal article |
| Kieli: | English |
| Julkaistu: |
AIP Publishing
2022
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| _version_ | 1826309181645258752 |
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| author | Hao, M Hope-Collins, J di Mare, L |
| author_facet | Hao, M Hope-Collins, J di Mare, L |
| author_sort | Hao, M |
| collection | OXFORD |
| description | This study presents a novel synthetic inflow generator capable of producing a random field matching a realistic set of two-point statistics with minimal input. The method is based on two main elements. The first element is a procedure to infer realistic two-point covariance tensors from readily available data (e.g. freestream velocity, boundary layer thickness, and turbulence intensity) by a preliminary Reynolds averaged Navier-Stokes (RANS) simulation with an explicit algebraic Reynolds stress model (EARSM) closure. The second element is an efficient eigen-decomposition step of the two-point correlation tensor which determines a set of modes.The modal decomposition guarantees the spatial correlation in transversal directions, while the temporal correlation/streamwise spatial correlation is obtained by digital filters based on longitudinal and transversal spectra of a realistic shapeand Taylor's hypothesis.The instantaneous inlet flow field is obtained by a linear combination of the modes via uncorrelated random weights with unit variance. The modes are generated in a computationally inexpensive pre-processing step.Compared to existing inflow generation methods that try to match given two-point statistics, the proposed method relieves the burden of obtaining data from direct numerical simulation (DNS) or experiments, while the complexity of the eigenvalue problem that needs to be solved is reduced.The proposed method is shown to produce a realistic turbulent channel flow and a realistic turbulent boundary layer by the large eddy simulation (LES), which contains statistics that are in good agreement with results from DNS.The proposed inflow generator features, cost-effectiveness, robustness, and potential for generalization to complex geometries. |
| first_indexed | 2024-03-07T07:31:53Z |
| format | Journal article |
| id | oxford-uuid:695d76d2-98b4-4457-b3ec-a933820d82d2 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:31:53Z |
| publishDate | 2022 |
| publisher | AIP Publishing |
| record_format | dspace |
| spelling | oxford-uuid:695d76d2-98b4-4457-b3ec-a933820d82d22023-01-23T07:14:45ZGeneration of turbulent inflow data from realistic approximations of the covariance tensorJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:695d76d2-98b4-4457-b3ec-a933820d82d2EnglishSymplectic ElementsAIP Publishing2022Hao, MHope-Collins, Jdi Mare, LThis study presents a novel synthetic inflow generator capable of producing a random field matching a realistic set of two-point statistics with minimal input. The method is based on two main elements. The first element is a procedure to infer realistic two-point covariance tensors from readily available data (e.g. freestream velocity, boundary layer thickness, and turbulence intensity) by a preliminary Reynolds averaged Navier-Stokes (RANS) simulation with an explicit algebraic Reynolds stress model (EARSM) closure. The second element is an efficient eigen-decomposition step of the two-point correlation tensor which determines a set of modes.The modal decomposition guarantees the spatial correlation in transversal directions, while the temporal correlation/streamwise spatial correlation is obtained by digital filters based on longitudinal and transversal spectra of a realistic shapeand Taylor's hypothesis.The instantaneous inlet flow field is obtained by a linear combination of the modes via uncorrelated random weights with unit variance. The modes are generated in a computationally inexpensive pre-processing step.Compared to existing inflow generation methods that try to match given two-point statistics, the proposed method relieves the burden of obtaining data from direct numerical simulation (DNS) or experiments, while the complexity of the eigenvalue problem that needs to be solved is reduced.The proposed method is shown to produce a realistic turbulent channel flow and a realistic turbulent boundary layer by the large eddy simulation (LES), which contains statistics that are in good agreement with results from DNS.The proposed inflow generator features, cost-effectiveness, robustness, and potential for generalization to complex geometries. |
| spellingShingle | Hao, M Hope-Collins, J di Mare, L Generation of turbulent inflow data from realistic approximations of the covariance tensor |
| title | Generation of turbulent inflow data from realistic approximations of the covariance tensor |
| title_full | Generation of turbulent inflow data from realistic approximations of the covariance tensor |
| title_fullStr | Generation of turbulent inflow data from realistic approximations of the covariance tensor |
| title_full_unstemmed | Generation of turbulent inflow data from realistic approximations of the covariance tensor |
| title_short | Generation of turbulent inflow data from realistic approximations of the covariance tensor |
| title_sort | generation of turbulent inflow data from realistic approximations of the covariance tensor |
| work_keys_str_mv | AT haom generationofturbulentinflowdatafromrealisticapproximationsofthecovariancetensor AT hopecollinsj generationofturbulentinflowdatafromrealisticapproximationsofthecovariancetensor AT dimarel generationofturbulentinflowdatafromrealisticapproximationsofthecovariancetensor |