The stochastic process of a turbulent flow in a pipeline provides a time series of the velocity field at any point of the domain by solving numerically the Navier-Stokes equation. The turbulent flow was produced by obstacles near the inlet, injecting eddies into the current. Moving downstream, these eddies evolve to a fully turbulent flow. Many length and time scales are involved in this process. We explore the cross-correlations of the velocity field time series at different points and also at different time scales using the detrended cross-correlation coefficient, ρDCCA, designed to analyze the cross-correlations in non-stationary time series. Thus, the results with ρDCCA allow interpreting how these eddies propagate downstream, and also quantify how adherent the velocity fields are with respect to the pipeline position.
This paper aims to detect memory loss of the symmetry of blockades in ducts and how far the information on the asymmetry of the obstacles travels in the turbulent flow from computational simulations with OpenFOAM. From a practical point of view, it seeks alternatives to detect the formation of obstructions in pipelines. The numerical solutions of the Navier–Stokes equations were obtained through the solver PisoFOAM of the OpenFOAM library, using the large Eddy simulation (LES) for the turbulent model. Obstructions were placed near the duct inlet and, keeping the blockade ratio fixed, five combinations for the obstacles sizes were adopted. The results show that the information about the symmetry is preserved for a larger distance near the ducts wall than in mid-channel. For an inlet velocity of 5[Formula: see text]m/s near the walls the memory is kept up to distance 40 times the duct width, while in mid-channel this distance is reduced almost by half. The maximum distance in which the symmetry breaking memory is preserved shows sensitivity to Reynolds number variations in regions near the duct walls, while in the mid channel that variations do not cause relevant effects to the velocity distribution.
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