  Modeling of fluid flows requires corresponding adequate and effective
approaches that would account for multiscale nature of the considered physics.
Despite the tremendous growth of computational power in the past decades,
modeling of fluid flows at engineering and system scales with a direct
resolution of all scales is still infeasibly computationally expensive. As a
result, several different physics-based methodologies were historically
suggested in an attempt to "bridge" the existing scaling gaps. In this paper,
the origin of the scaling gaps in computational fluid dynamics and thermal
hydraulics (with an emphasis on engineering scale applications) is discussed.
The discussion is supplemented by a review, classification, and discussion of
the physics-based multiscale modeling approaches. The classification is based
on the existing in literature ones and distinguishes serial and concurrent
approaches (based on the information flow between micro- and macro-models) with
their variations. Possible applications of the data-driven (machine learning
and statistics-based) methods for enabling and / or improving multiscale
modeling (bridging the scaling gaps) are reviewed. The introduced
classification distinguishes error correction (hybrid) models; closure modeling
(turbulence models, two-phase and thermal fluid closures); and approaches aimed
at the facilitation of the concurrent physics-based multiscale modeling
approaches. A comprehensive review of data-driven approaches for turbulence
modeling is performed.
