Data-driven enhancement of coherent structure-based models for predicting instantaneous wall turbulence
نویسندگان
چکیده
Predictions of the spatial representation instantaneous wall-bounded flows, via coherent structure-based models, are highly sensitive to geometry representative structures employed by them. In this study, we propose a methodology extract three-dimensional (3-D) statistically significant eddies from multi-point wall-turbulence datasets, for direct implementation into these models improve their predictions. The is here reconstructing 3-D statistical picture inertial wall turbulence all canonical across decade friction Reynolds number (Re?). These responsible Re?-dependence skin-friction drag and also facilitate inner-outer interactions, making them key targets models. empirical analysis brings out geometric self-similarity large-scale wall-coherent motions suggests hairpin packet as flow structure thereby aligning with framework on which attached eddy model (AEM) based. same extended very-large-scaled motions, consideration differences in internal versus external flows. Implementation empirically-obtained scalings large AEM shown enhance predictions three velocity components. Finally, an active control system driven conceptualized, towards favourably altering influence drag.
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ژورنال
عنوان ژورنال: International Journal of Heat and Fluid Flow
سال: 2021
ISSN: ['1879-2278', '0142-727X']
DOI: https://doi.org/10.1016/j.ijheatfluidflow.2021.108879