Uncovering the parameter variations of the inner–outer interaction model across incompressible and compressible wall-bounded turbulence

The inner–outer interaction model (IOIM), first proposed by Marusic et al. ( Science , 2010, vol. 329, pp. 193–196), has proven to be an effective turbulence model for canonical and non-canonical wall-bounded flows, where a reference velocity signal from the logarithmic region acts as the input for predicting near-wall velocity fluctuations. Its most recent iteration by Baars et al. ( Phys. Rev. Fluids , 2016, vol. 1, p. 054406) further proposes a user-independent scale separation point, refining model parameters. In this study, we compared the long-perceived universal IOIM’s parameters, including the linear transfer kernel, amplitude modulation coefficients and the universal signal for a range of Reynolds and Mach numbers, where mathematical relationships between the parameters are proposed. We observed that while the universal signals exhibit a high degree of similarity, particularly near the wall, the amplitude modulation coefficients and linear transfer kernels display Reynolds and Mach number effects, where varying the reference location also causes them to exhibit significant changes. We have found transformations to collapse amplitude modulation coefficients for incompressible flows and differing reference locations, improving modelling via the IOIM across flow parameters. Despite this, compressibility effects cannot be suitably accounted for currently and remain a future challenge for the IOIM framework.

Authors

Institutions

Publication Details

Journal
Journal of Fluid Mechanics
Published
2026-04-27
DOI
https://doi.org/10.1017/jfm.2026.11452
Primary Topic
Fluid Dynamics and Turbulent Flows
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Uncovering the parameter variations of the inner–outer interaction model across incompressible and compressible wall-bounded turbulence

Lin Fu, Anjia Ying, Justin E. Ka Ip Sun
Journal of Fluid Mechanics
Fluid Dynamics and Turbulent Flows
article

Uncovering the parameter variations of the inner–outer interaction model across incompressible and compressible wall-bounded turbulence

Lin Fu, Anjia Ying, Justin E. Ka Ip Sun
article en

Abstract

The inner–outer interaction model (IOIM), first proposed by Marusic et al. ( Science , 2010, vol. 329, pp. 193–196), has proven to be an effective turbulence model for canonical and non-canonical wall-bounded flows, where a reference velocity signal from the logarithmic region acts as the input for predicting near-wall velocity fluctuations. Its most recent iteration by Baars et al. ( Phys. Rev. Fluids , 2016, vol. 1, p. 054406) further proposes a user-independent scale separation point, refining model parameters. In this study, we compared the long-perceived universal IOIM’s parameters, including the linear transfer kernel, amplitude modulation coefficients and the universal signal for a range of Reynolds and Mach numbers, where mathematical relationships between the parameters are proposed. We observed that while the universal signals exhibit a high degree of similarity, particularly near the wall, the amplitude modulation coefficients and linear transfer kernels display Reynolds and Mach number effects, where varying the reference location also causes them to exhibit significant changes. We have found transformations to collapse amplitude modulation coefficients for incompressible flows and differing reference locations, improving modelling via the IOIM across flow parameters. Despite this, compressibility effects cannot be suitably accounted for currently and remain a future challenge for the IOIM framework.

Journal of Fluid MechanicsVol. 1034
Hong Kong University of Science and Technology (HK)
National Natural Science Foundation of China
Sustainable cities and communities
Openalex Percentile: Top 13%
Fluid Dynamics and Turbulent Flows
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.