  We introduce a closure model for wall-modeled large-eddy simulation (WMLES),
referred to as the Building-block Flow Model (BFM). The foundation of the model
rests on the premise that a finite collection of simple flows encapsulates the
essential physics necessary to predict more complex scenarios. The BFM is
implemented using artificial neural networks and introduces five advancements
within the framework of WMLES: (1) It is designed to predict multiple flow
regimes (wall turbulence under zero, favorable, adverse mean-pressure-gradient,
and separation); (2) It unifies the closure model at solid boundaries (i.e.,
the wall model) and the rest of the flow (i.e., the subgrid-scale model) into a
single entity; (3) It ensures consistency with numerical schemes and gridding
strategy by accounting for numerical errors; (4) It is directly applicable to
arbitrary complex geometries; (5) It can be scaled up to model additional flow
physics in the future if needed (e.g., shockwaves and laminar-to-turbulent
transition). The BFM is utilized to predict key quantities of interest in
turbulent channel and pipe flows, a Gaussian bump, a simplified aircraft, and a
realistic aircraft in landing configuration. In all cases, the BFM demonstrates
similar or superior capabilities in terms of accuracy and computational
efficiency compared to previous state-of-the-art closure models.
