Data driven modeling of junction flows

Master thesis (2022)

Authors

G.M.J.S. Vigner Aerospace Engineering

Contributors

R.P. Dwight Aerodynamics - Aerospace Engineering (supervisor 1)
Faculty
Aerospace Engineering
Copyright: © 2022 Geneviève Vigner
LES Data-Driven Junction flows
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Published Date

25-02-2022

Language

English

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Faculty

Aerospace Engineering

Abstract

A wall resolved LES simulation of the Anti-Fairing wing/body junction introduced by Belligoli et al. [6] to reduce interference drag is performed. The LES mesh is composed of 61.7 million cells with a C-fitted grid around the wing . The simulation is performed using the pimple solver of Open- Foam 4 with a time and space varying inlet boundary condition obtained thanks to a precursor. This simulation will be used to assess the impact of the Anti-Fairing by comparing the result to the wall resolved baseline case of Alberts [2] and to serve as a training data for data driven techniques applied to junctions flows. Using the wall resolved LES we apply the data driven algorithm method Sparse Regression of Turbulent Stress Anisotropy (SpaRTA) developed by Schmelzer et al. [38] in the case of junction flows. It is shown that the first step of the method, namely the k-corrective frozen RANS, is able to produce corrective fields to the Reynolds tensor and the turbulent kinetic energy equations in this case. The corrective fields once added in a k-omega SST simulation make it possible to obtain the exact location, strength and shape of the main horseshoe vortex. The upstream boundary layer is also subject to corrections indicating RANS-LES mismatch in the inflow. Mutual Information (MI) is calculated to identify the relevant tensors, physical features and invariants that correlate with the junction flow data. Finally, algebraic models for the corrective fields are obtained. They are compared to the true values of these fields. It is possible to see that the performance of SpARTA models is good upstream of the wing. However, models found and tested in the vicinity of the wing, where the separation and horseshoe vortex are located, are not fully able to capture the relevant corrections. Additional constraints or steps to the ones performed in the time of this study may be necessary in order to use SpaRTA to generate models giving improved predictions compared to classic RANS turbulence models.