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=Lib:Wing-body junction=
=Wing-body junction=
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= Abstract =
= Abstract =


This entry features the flow around a 3D wing-body junction based on the configuration considered in [[lib:DNS_1-6#1|Apsley and 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.  
This entry features the flow around a 3D wing-body junction based on the configuration considered in [[DNS_1-6#1|Apsley and Leschziner (2001)]], whose geometry consists of a semi-elliptical 3:2 nose with a NACA0020 tail profile mounted on a flat plate. 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.  


An under-resolved Direct Numerical Simulation (uDNS) of this test case has been computed using the high-order discontinuous Galerkin (DG) code MIGALE, see [[lib:DNS_1-6#2|Bassi ''et al.'' (2016)]]. The code couples the DG spatial discretization with a high-order implicit time integration, which relies on Rosenbrock schemes.
An under-resolved Direct Numerical Simulation (uDNS) of this test case has been performed using the high-order discontinuous Galerkin (DG) code MIGALE, see [[DNS_1-6#2|Bassi ''et al.'' (2016)]]. The code couples the DG spatial discretization with a high-order implicit time integration, which relies on Rosenbrock schemes.


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, although computed for half of the experimental incoming boundary layer height, and to be exploited by Machine Learning or data-assimilation techniques to improve standard RANS models.
The present database has been created with the aim of providing more thorough information on the flow field with respect to existing experiments, albeit computed for half of the experimental incoming boundary layer thickness and hence not directly comparable with the experiments, and of allowing for exploitation by Machine Learning or data-assimilation techniques to improve standard RANS models.


The provided statistical quantities in the database are:
The provided statistical quantities in the database are:
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|organisation=University of Bergamo (UNIBG), ONERA
|organisation=University of Bergamo (UNIBG), ONERA
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Latest revision as of 11:37, 27 February 2023

Wing-body junction

Front Page

Description

Computational Details

Quantification of Resolution

Statistical Data

Instantaneous Data

Storage Format


Abstract

This entry features the flow around a 3D wing-body junction based on the configuration considered in Apsley and Leschziner (2001), whose geometry consists of a semi-elliptical 3:2 nose with a NACA0020 tail profile mounted on a flat plate. 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.

An under-resolved Direct Numerical Simulation (uDNS) of this test case has been performed using the high-order discontinuous Galerkin (DG) code MIGALE, see Bassi et al. (2016). The code couples the DG spatial discretization with a high-order implicit time integration, which relies on Rosenbrock schemes.

The present database has been created with the aim of providing more thorough information on the flow field with respect to existing experiments, albeit computed for half of the experimental incoming boundary layer thickness and hence not directly comparable with the experiments, and of allowing for exploitation by Machine Learning or data-assimilation techniques to improve standard RANS models.

The provided statistical quantities in the database are:

  • mean pressure and velocity components;
  • Reynolds stress components;
  • Taylor microscale;
  • Kolmogorov length and time scales;

As the solver discretizes the compressible Navier-Stokes equations, density and temperature fields, as well their gradients, have been collected during the computational campaign. However, since the flow regime is incompressible (), these fields are a side product of this contribution and thus are not reported.

DNS 1-6 instantaneus skin friction magnitude.png
Figure 1: Wing-body junction, Re=115,000. Detail of the instantaneous skin friction magnitude at the leading edge of the blade using MIGALE with DG P3 (~1.08 billion DoF/eqn).

University of Bergamo acknowledges PRACE for awarding the access to Hawk hosted by GCS at HLRS, Germany.

References

  1. Apsley, D.D. and Leschziner, M. (2001): Investigation of Advanced Turbulence Models for the Flow in a Generic Wing-Body Junction. Flow, Turbulence and Combustion, Vol. 67, pp. 25–55
  2. Bassi, F., Botti, L., Colombo, A. C, Ghidoni, A. and Massa, F. (2016): On the development of an implicit high-order Discontinuous Galerkin method for DNS and implicit LES of turbulent flows. European Journal of Mechanics, B/Fluids, Vol. 55(2), pp. 367-379




Contributed by: Francesco Bassi (UNIBG), Alessandro Colombo (UNIBG), Francesco Carlo Massa (UNIBG), Jean-Baptiste Chapelier (ONERA) — University of Bergamo (UNIBG), ONERA

Front Page

Description

Computational Details

Quantification of Resolution

Statistical Data

Instantaneous Data

Storage Format


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