Classical RANS (Reynolds-Averaged Navier-Stokes) turbulence models have limited accuracy in the prediction of the flow over the wing-body geometry. Therefore, this work focuses on improving the prediction accuracy of the classical k-ω SST turbulence model for the junction flow by means of the data-driven method SpaRTA. In the SpaRTA methodology higher-fidelity data, in this work LES (Large Eddy Simulation) data, is leveraged to find correction models
that enhance the baseline turbulence model. These correction models aim at correcting the Reynolds stress anisotropy and the turbulent kinetic energy (k) equation. The correction models are obtained by sparsely regressing flow features to the correction fields that are found by the k-corrective frozen approach.

The results showed that the correction fields determined by the frozen method are very effective. Direct propagation of these fields resulted in a solution very similar to the LES data. In contrast to the model search for the k correction, finding a model for the anisotropy correction by means of sparse regression was difficult. The latter is reflected in the CFD (Computation Fluid Dynamics) propagation of the found models on the same flow case as the training of the models. This model propagation showed an improvement in the placement of the horseshoe vortex, however, the vortex topology in the corner region was unsatisfactory. Which is linked to the poor model fit of the anisotropy correction field.

Besides the search for an improved turbulence model, this work also encapsulates an evaluation of the drag reduction by the anti-fairing geometry for the wing-body junction flow. This evaluation was achieved by comparing available wall-resolved LES data sets. The evaluation revealed that the anti-fairing reduces the drag force by a propulsive pressure mechanism over the bottom wall for a specific geometry over which the drag is computed. This geometry included only 5% of the complete wing span, for larger wing spans the drag reduction was not evident.