Data-driven identification of multiscale self-similarity and asymptotic matching

Abstract

A central problem in fluid mechanics is the identification of self-similarity, which reveals the underlying scaling behaviour of a flow. Recently, a data-driven framework proposed by Bempedelis et al. (2025 J. Fluid Mech., vol. 1020, A11) has enabled the extraction of single-scale self-similarity from data, without prior knowledge of the governing equations. However, many physical systems are inherently multiscale and therefore require more general approaches. Building on this foundation, we develop an algorithm for the systematic identification of multiscale self-similarity. The algorithm is first applied to two canonical fluid-mechanical problems: turbulent channel flow and late-stage homogeneous decaying turbulence characterised by classical dissipation laws. In both cases, the algorithm successfully identifies inner and outer self-similarity from numerical data and recovers the corresponding similarity expressions. In the intermediate region of both problems, the algorithm identifies similarity expressions that are independent of both the inner and outer scales, providing a novel data-driven route to recovering the corresponding scaling laws: the logarithmic law in turbulent channel flow and the -5/3 law in late-stage homogeneous decaying turbulence. Moreover, the algorithm uncovers corrections to both laws: one linked to the power-law scaling proposed by Barenblatt (1993 J. Fluid Mech., vol. 248, 513--520), and the other yielding an improved approximation of the energy spectrum. The algorithm is then applied to early-stage homogeneous decaying turbulence, characterised by the nonclassical dissipation law. It identifies the same inner similarity as in the late stage, but different outer similarity expressions.