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Predictions of the spatial representation of instantaneous wall-boundedflows, via coherent structure-based models, are highly sensitive to thegeometry of the representative structures employed by them. In this study, wepropose a methodology to extract the three-dimensional (3-D) geometry of thestatistically significant eddies from multi-point wall-turbulence datasets, fordirect implementation into these models to improve their predictions. Themethodology is employed here for reconstructing a 3-D statistical picture ofthe inertial wall coherent turbulence for all canonical wall-bounded flows,across a decade of friction Reynolds number ($Re_{\tau}$). These structures areresponsible for the $Re_{\tau}$-dependence of the skin-friction drag and alsofacilitate the inner-outer interactions, making them key targets ofstructure-based models. The empirical analysis brings out the geometricself-similarity of the large-scale wall-coherent motions and also suggests thehairpin packet as the representative flow structure for all wall-bounded flows,thereby aligning with the framework on which the attached eddy model (AEM) isbased. The same framework is extended here to also model the very-large-scaledmotions, with a consideration of their differences in internal versus externalflows. Implementation of the empirically-obtained geometric scalings for theselarge structures into the AEM is shown to enhance the instantaneous flowpredictions for all three velocity components. Finally, an active flow controlsystem driven by the same geometric scalings is conceptualized, towardsfavourably altering the influence of the wall coherent motions on theskin-friction drag.

Data-driven enhancement of coherent structure-based models for predicting instantaneous wall turbulence