Wall-Resolved Large Eddy Simulation of a Wing-Body Junction

High-Fidelity Data Generation for Data-Driven Turbulence Modelling

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Abstract

A wall-resolved Large Eddy Simulation (LES) of a wing-body junction is performed. The aim is to generate high-fidelity junction flow data to be used in a data-driven turbulence modelling approach, specifically to improve the accuracy of RANS-simulations in junction flows. The simulation is performed on a 61.5 million C-grid body fitted mesh in the pimpleFoam solver of OpenFOAM, with a turbulent channel flow precursor providing the unsteady inlet boundary condition. Analysis of the wall-resolved LES shows that the simulation accurately captures the complex flow phenomena in the wing-body junction flow including intermittency for the present inflow condition. Comparisons of the wall-resolved LES with a coarse-grid RANS simulation and the wall-modelled LES of Srikumar [2019] show that the wall-resolved LES in the present study is an improvement over the other two numerical methods. Most notably, an improvement in terms of the prediction of the location and magnitude of the mean spanwise vorticity and the mean turbulent kinetic energy of the horseshoe vortex systems was observed. Especially the RANS-simulation was unable to accurately capture the complex flow physics in the junction due to the limitations of RANS-methods, which are unable to accurately capture Reynolds stress anisotropy due to the Boussinesq hypothesis. An analysis of the high-fidelity junction flow data was performed to indicate regions where the Boussinesq hypothesis breaks down. The most notable region where the Boussinesq hypothesis was found to be not valid, was the region in close proximity to the wing-body junction upstream of the wing. Due to the breakdown of the Boussinesq hypothesis in the junction region, significant improvements of the accuracy of junction flow RANS-simulations can potentially be achieved by using the high-fidelity data from the present study in a data-driven turbulence modelling approach.