  A wall model for large-eddy simulation (LES) is proposed by devising the flow
as a combination of building blocks. The core assumption of the model is that a
finite set of simple canonical flows contains the essential physics to predict
the wall-shear stress in more complex scenarios. The model is constructed to
predict zero/favourable/adverse mean pressure gradient wall turbulence,
separation, statistically unsteady turbulence with mean flow
three-dimensionality, and laminar flow. The approach is implemented using two
types of artificial neural networks: a classifier, which identifies the
contribution of each building block in the flow, and a predictor, which
estimates the wall-shear stress via combination of the building-block flows.
The training data are directly obtained from wall-modelled LES (WMLES)
optimised to reproduce the correct mean quantities. This approach guarantees
the consistency of the training data with the numerical discretisation and the
gridding strategy of the flow solver. The output of the model is accompanied by
a confidence score in the prediction that aids the detection of regions where
the model underperforms. The model is validated in canonical flows (e.g.
laminar/turbulent boundary layers, turbulent channels, turbulent
Poiseuille-Couette flow, turbulent pipe) and two realistic aircraft
configurations: the NASA Common Research Model High-lift and NASA Juncture Flow
experiment. It is shown that the building-block-flow wall model outperforms (or
matches) the predictions by an equilibrium wall model. It is also concluded
that further improvements in WMLES should incorporate advances in subgrid-scale
modelling to minimise error propagation to the wall model.
