UFR 3-36: Difference between revisions
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= Abstract = | = 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. [‌[[Lib:UFR_3-36_References#1|1]]][‌[[Lib:UFR_3-36_References#2|2]]] as well as the curved backward facing step [‌[[Lib:UFR_3-36_References#3|3]]][‌[[Lib:UFR_3-36_References#4|4]]]. For both cases, experimental data, LES/DNS-data as well as results from RANS turbulence models exist [‌[[Lib:UFR_3-36_References#4|4]]][‌[[Lib:UFR_3-36_References#5|5]]]. | 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. [‌[[Lib:UFR_3-36_References#1|1]]][‌[[Lib:UFR_3-36_References#2|2]]] as well as the curved backward facing step [‌[[Lib:UFR_3-36_References#3|3]]][‌[[Lib:UFR_3-36_References#4|4]]]. For both cases, experimental data, LES/DNS-data as well as results from RANS turbulence models exist [‌[[Lib:UFR_3-36_References#4|4]]][‌[[Lib:UFR_3-36_References#5|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 [‌[[Lib:UFR_3-36_References#6|6]]]. | 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 (<math>{Re_H=78490}</math> and <math>{Re_H=136504}</math>) 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 [‌[[Lib:UFR_3-36_References#6|6]]]. | ||
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Revision as of 15:41, 2 November 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 ( and ) 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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