Information transfer between turbulent boundary layers and porous media

Wang, Wenkang; Chu, Xu; Lozano-Durán, Adrián; Helmig, Rainer

doi:10.1017/jfm.2021.445

Cited by 18 publications

(22 citation statements)

References 39 publications

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“…There is a strong correlation between the v of nearby upstream and downstream pore units for all cases tested. As discussed by Wang et al (2021a), this strong correlation can be attributed to the interaction of fluids in adjacent pores under the constraint of continuity. A strong up-welling motion at one pore could induce down-welling motions of nearby pore units.…”

Section: Spatial Structure Of Transfer Entropymentioning

confidence: 84%

“…However, finding the connection between scale and causality is a non-trivial problem. Earlier attempts to obtain the desired frequency-resolved transfer entropy relied on narrow-band filtering of data from the information source and the information receiving target, followed by feeding the resulting narrow-band signals into a transfer entropy analysis (Besserve et al 2010;Wang et al 2021a). However, this method could be sensitive to the choice of the band filter.…”

Section: Surrogate Methods Based On Discrete Wavelet Transformmentioning

confidence: 99%

“…This is reasonable since the fluid ejected by the up-welling motion can be convected by the mean velocity of the channel. Wang et al (2021a) inspected the full spectra of the channel flow and porous medium flow. They showed that the energy of the porous medium flow concentrates on the low-frequency modes, and has a large disparity with the central frequency of near-wall turbulence.…”

Section: Optimal Information Transfer Locationsmentioning

confidence: 99%

“…The settings of time lags and normalization are also optimized to achieve conservation of information. Wang et al (2021a) used transfer entropy as a marker to evaluate the causal interaction between turbulent channel flow and porous media consisting of circular cylinders. The POD time coefficients of the leading-order modes were extracted as the representative signals for surface and subsurface flow.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are still numerous challenges and unanswered questions. In the work of Wang et al (2021a), the source and target signals are selected as the time coefficients of the orthogonal basis of the flow fields. For the leading-order modes investigated, the spatial location is fixed for each pore unit, and the scale of flow motions are constrained to a relatively large-scale range.…”

Section: Introductionmentioning

confidence: 99%

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Spatial and spectral characteristics of information flux between turbulent boundary layers and porous media

et al. 2022

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The interaction between boundary layer turbulence and a porous layer is the cornerstone of interface engineering. In this study, the spatial and spectral-resolved transfer entropy is used to assess the asymmetry of the causal interaction next to the permeable wall. The analysis is based on pore-resolved direct numerical simulation of turbulent channel flow over cylinder arrays. The spatial map of transfer entropy reveals the information flux between the porous medium and arbitrary nearby positions, and paths connecting locations with maximum information transfer are identified. The paths in the ‘top-down’ and ‘bottom-up’ directions, respectively, lean upstream and downstream, demonstrating that the coupling process is directionally dependent. The scale dependence of transfer entropy is inspected with a surrogate data strategy. As wall permeability increases, the active scale range in causal interaction shifts from near-wall vortices to Kelvin–Helmholtz type eddies. In addition, linear stochastic estimation is used to determine the statistical velocity field for a local informative event. In an average sense, the interaction between a convecting sweep or ejection event and the up/down-welling motions at the pore unit is the core mechanism that contributes to the causal coupling. The statistical findings derived from the transfer entropy are then validated using a neural network-based remote sensing model.

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Section: Spatial Structure Of Transfer Entropymentioning

confidence: 84%

Section: Surrogate Methods Based On Discrete Wavelet Transformmentioning

confidence: 99%

Section: Optimal Information Transfer Locationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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