UFR 3-36: Difference between revisions
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{{ | {{The UFR studied here, is a Turbulent Boundary layer (TBL) subjected to an adverse pressure gradient (APG) inducing flow separation on a smooth curved surface. The physically and industrially significant flow phenomenon is challenging to predict with state-of-the-art RANS turbulence models despite the numerous existing experimental and numerical studies. Popular examples are the 2D NASA Wall-mounted Hump of Greenblatt et al. [1], [2] as well as the curved backward facing step [3] [4]. For both cases, experimental data, LES/DNS-data as well as results from RANS turbulence models exist [4] [5]. | ||
Opposed to the test cases referred to before, the test case described here is designed as a purely numerical test case that cannot be directly transferred to a wind tunnel experiment. A family of four different geometries with two different Reynolds numbers (Re_H=78490 and Re_H=136504) based on the step height H was designed. The objective is to provide a test case suitable for DNS computations to generate a comprehensive database that can be exploited by data-driven approaches employing Machine Learning (ML). The final designs are based on a study applying several state-of-the-art Reynold-Averaged Navier-Stokes (RANS) models as well as on an experimental test case designed by NASA [6]. | |||
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Revision as of 09:16, 5 September 2022
HiFi-TURB-DLR rounded step
Semi-confined Flows
Underlying Flow Regime 3-36
Abstract
{{The UFR studied here, is a Turbulent Boundary layer (TBL) subjected to an adverse pressure gradient (APG) inducing flow separation on a smooth curved surface. The physically and industrially significant flow phenomenon is challenging to predict with state-of-the-art RANS turbulence models despite the numerous existing experimental and numerical studies. Popular examples are the 2D NASA Wall-mounted Hump of Greenblatt et al. [1], [2] as well as the curved backward facing step [3] [4]. For both cases, experimental data, LES/DNS-data as well as results from RANS turbulence models exist [4] [5].
Opposed to the test cases referred to before, the test case described here is designed as a purely numerical test case that cannot be directly transferred to a wind tunnel experiment. A family of four different geometries with two different Reynolds numbers (Re_H=78490 and Re_H=136504) based on the step height H was designed. The objective is to provide a test case suitable for DNS computations to generate a comprehensive database that can be exploited by data-driven approaches employing Machine Learning (ML). The final designs are based on a study applying several state-of-the-art Reynold-Averaged Navier-Stokes (RANS) models as well as on an experimental test case designed by NASA [6].
}}
Contributed by: Erij Alaya and Cornelia Grabe — Deutsches Luft-und Raumfahrt Zentrum (DLR)
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