  The prediction of aircraft aerodynamic quantities of interest remains among
the most pressing challenges for computational fluid dynamics. The aircraft
aerodynamics are inherently turbulent with mean-flow three-dimensionality,
often accompanied by laminar-to-turbulent transition, flow separation,
secondary flow motions at corners, and shock wave formation, to name a few.
However, the most widespread wall models are built upon the assumption of
statistically-in-equilibrium wall-bounded turbulence and do not faithfully
account for the wide variety of flow conditions described above. This raises
the question of how to devise models capable of accounting for such a vast and
rich collection of flow physics in a feasible manner. In this work, we propose
tackling the wall-modeling challenge by devising the flow as a collection of
building blocks, whose information enables the prediction of the stress as the
wall. The model relies on the assumption that simple canonical flows contain
the essential flow physics to devise accurate models. Three types of building
block units were used to train the model: turbulent channel flows, turbulent
ducts and turbulent boundary layers with separation. This limited training set
will be extended in future versions of the model. The approach is implemented
using two interconnected artificial neural networks: a classifier, which
identifies the contribution of each building block in the flow; and a
predictor, which estimates the wall stress via non-linear combinations of
building-block units. The output of the model is accompanied by the confidence
in the prediction. The latter value aids the detection of areas where the model
underperforms, such as flow regions that are not representative of the building
blocks used to train the model. The model is validated in a unseen case
representative of external aerodynamic applications: the NASA Juncture Flow
Experiment.
