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We investigate the flux of kinetic energy across length scales in a turbulentpipe flow. We apply explicit spatial filtering of DNS data and assess theeffect of different filter kernels (Fourier, Gauss, box) on the local structureof the inter-scale energy flux ($\Pi$) and its statistics. Qualitatively, themean energy flux at each wall-normal distance is robust with respect to thefilter kernel, whereas there are significant differences in the estimatedintensity and distribution of localised $\Pi$ events. We find conflictingcorrelations between typical flow structures in the buffer layer (streaks,vortices and different $Q$ events) and regions of forward/backward transfer inthe instantaneous $\Pi$ field. In particular, cross-correlations are highlyupstream-downstream symmetric for the Fourier kernel, but asymmetric for theGauss and box kernel. We show that for the Gauss and box kernel, $\Pi$ eventspreferably sit on the inclined meander at the borders of streaks where strongshear layers occur, whereas they appear centred on top of the streaks for theFourier kernel. Moreover, using the Fourier kernel we reveal a directcoincidence of backward scatter and fluid transport away from the wall($Q_{1}$), which is absent for the Gauss and the box kernel. However, allkernels equally predict backward scatter directly downstream of $Q_{1}$ events.Our findings expand the common understanding of the wall cycle and might impactmodelling and control strategies. Altogether, our results suggest thatinterpretations of the inter-scale energy flux relying on Fourier filtersshould be taken with caution, because Fourier filters act globally in physicalspace, whereas $\Pi$ events are strongly spatially localised. Our Pythonpost-processing tool eFlux for scale separation and flux computations in pipeflows is freely available and can be easily adapted to other flow geometries.

How does filtering change the perspective on the scale-energetics of the near-wall cycle?