DNS 1-6: Difference between revisions
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This entry features DNS of a 3D wing-body junction flow based on the flow configuration considered in Apsley & Leschziner (2001), which geometry consists in a NACA0020 profile mounted on a flat plate. The wing-body junction flow problems are relevant for the prediction of corner flow separation encountered in applications of aeronautical interest. This flow is highly 3D and anisotropic regarding the turbulent stresses, which poses a challenge to existing RANS modeling approaches. The present database has been created with the aim to allow for a more thorough availability of the flow field with respect to existing experiments and to be exploited by Machine Learning or data-assimilation techniques to improve standard RANS models. | This entry features DNS of a 3D wing-body junction flow based on the flow configuration considered in Apsley & Leschziner (2001), which geometry consists in a semi-elliptical 3:2 nose with a NACA0020 tail profile mounted on a flat plate. The wing-body junction flow problems are relevant for the prediction of corner flow separation encountered in applications of aeronautical interest. This flow is highly 3D and anisotropic regarding the turbulent stresses, which poses a challenge to existing RANS modeling approaches. The present database has been created with the aim to allow for a more thorough availability of the flow field with respect to existing experiments and to be exploited by Machine Learning or data-assimilation techniques to improve standard RANS models. | ||
Revision as of 14:20, 10 February 2023
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Abstract
This entry features DNS of a 3D wing-body junction flow based on the flow configuration considered in Apsley & Leschziner (2001), which geometry consists in a semi-elliptical 3:2 nose with a NACA0020 tail profile mounted on a flat plate. The wing-body junction flow problems are relevant for the prediction of corner flow separation encountered in applications of aeronautical interest. This flow is highly 3D and anisotropic regarding the turbulent stresses, which poses a challenge to existing RANS modeling approaches. The present database has been created with the aim to allow for a more thorough availability of the flow field with respect to existing experiments and to be exploited by Machine Learning or data-assimilation techniques to improve standard RANS models.
Contributed by: Alessandro Colombo (UNIBG), Francesco Carlo Massa (UNIBG), Francesco Bassi (UNIBG), Jean-Baptiste Chapelier (ONERA) — University of Bergamo (UNIBG), ONERA
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