A turbulent quarter century of active grids: from Makita (1991) to the present

Mydlarski, Laurent

doi:10.1088/1873-7005/aa7786

Cited by 52 publications

(37 citation statements)

References 87 publications

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“…A schematic sketch of the experimental setup is shown in figure 1a. Turbulence is produced by means of an active grid [20] operated in triple random or open mode [21] (using both grid protocols, we recovered K41 [22,23] turbulence at the measuring locations), downstream of which a rack of 36 spray nozzles generate inertial water droplets with a polydisperse diameter distribution. This polydispersity was previously quantified by means of a PDI (see [1,2]), and the measurements for this study are illustrated in figure 1b.…”

Section: The Experimental Setup and Methodsmentioning

confidence: 99%

Pitfalls Measuring 1D Inertial Particle Clustering

Mora

Aliseda

Cartellier

et al. 2019

Springer Proceedings in Physics

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Clustering is an important phenomenon in turbulent flows laden with inertial particles. Although this process has been studied extensively, there are still many open questions about both the fundamental physics and the reconciliation of different observations into a coherent quantitative view of this important mechanism for particle-turbulence interaction, that can enable high resolution modeling.In this work, we study the effect of projecting this phenomenon onto 2D and 1D (as usually done in experimental measurements). In particular, the effect of measurement volume in 1D projections on detected cluster properties, such as size or concentration, is explored to provide a method for comparison of published and future observations, from experimental or numerical data. The results demonstrate that, in order to capture accurate values of the mean cluster properties under a wide range of experimental conditions, the measurement volume needs to be larger than the Kolmogorov length scale (η), and smaller than about ten percent of the integral length scale of the turbulence L. This dependency provides the correct scaling to carry out unidimensional measurements of preferential concentration, taking into account the turbulence characteristics.Additionally, it is critical to disentangle the cluster-characterizing results from random contributions to the cluster statistics, specially in 1D, as the raw probability density function (PDF) of Voronoï cells does not provide error-free information on the clusters size or local concentration. We propose a methodology to correct for this measurement bias, with an analytical model of the cluster PDF obtained from comparison with a Random Poisson Process (RPP) probability distribution in 1D, which appears to discard the existence of power laws in the cluster PDF. At higher dimensions, 2 or 3 D, power law tails were found with our cluster identification algorithm. We develop a new test to discern between turbulence-driven clustering and randomness, that complements the cluster identification algorithm by segregating the number of particles inside each cluster.

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Section: The Experimental Setup and Methodsmentioning

confidence: 99%

Pitfalls Measuring 1D Inertial Particle Clustering

Mora

Aliseda

Cartellier

et al. 2019

Springer Proceedings in Physics

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“…Since the first active grid was proposed by Makita [10], active-grid-generated turbulence has become a standard way to generate moderate-to-high Reynolds numbers in wind/water tunnels [14]. They present several advantages for wind/water tunnels research, as they allow to obtain bespoke unstationary/inhomogeneous turbulence and generate large values of Reynolds numbers based on the Taylor microscale Re λ while keeping reasonable homogeneous isotropic turbulence (HIT) conditions.…”

Section: Introductionmentioning

confidence: 99%

Energy cascades in active-grid-generated turbulent flows

et al. 2019

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The energy cascade and diverse turbulence properties of active-grid-generated turbulence were studied in a wind tunnel via hot-wire anemometry. To this aim, two active grid protocols were considered. The first protocol is the standard triple-random mode, where the grid motors are driven with random rotation rates and directions, which are changed randomly in time. This protocol has been extensively used due to its capacity to produce higher values of Re λ than its passive counter part, with good statistical homogeneity and isotropy. The second protocol was a static or open grid mode, where all grid blades were completely open, yielding the minimum blockage attainable with our grid.Centreline streamwise profiles were measured for both protocols and several inlet velocities. It was found that the turbulent flow generated with the triple-random protocol evolved in the streamwise direction consistently with an energy dissipation scaling of the form ε = C ε u 3 /L, with C ε being a constant, L the longitudinal integral length-scale, and u the rms of the longitudinal velocity fluctuations.Conversely for the open-static grid mode, the energy dissipation followed a non-equilibrium turbulence scaling, namely, C ε ∼ Re G /Re L , where Re G is a global Reynolds number based on the inlet conditions of the flow, and Re L is based on the local properties of the flow downstream the grid. Furthermore, this open-static grid mode scaling exhibits important differences with other grids, as the downstream location of the peak of turbulence intensity is a function of the inlet velocity, a remarkable observation not previously reported, as it would allow to study the underlying principles of the transition between equilibrium and non-equilibrium scalings, which are yet to be understood.It was also found that a rather simple theoretical model can predict the value of C ε based on the number density of zero-crossings of the longitudinal velocity fluctuations. A theory, which is valid for both active grid operating protocols (and therefore two different energy cascades).

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“…Most grids will have some spatial, large-scale periodicity, which can be grossly modeled by a sine-function. The case of active grids [28,29] is particular since in this case, in addition to blockage, the rotating shafts of the grid will induce a large transverse turbulent diffusion. The sinelike profiles are probably less prominent for such grids if the stirring is intensive.…”

Section: A Simplified Description Of the Mean Flowmentioning

confidence: 99%

Production and dissipation of kinetic energy in grid turbulence

Bos¹

2020

Phys. Rev. Fluids

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In grid turbulence, not so far behind the grid, an average flow can be observed with a close to sinusoidal velocity profile, corresponding to the wakes behind the grid bars. The kinetic energy of this mean flow decays rapidly and a close to isotropic flow is observed further downstream. We show how these wakes behind the grid bars influence the downstream turbulence. In particular, we investigate the decay rate of kinetic energy, the behavior of the normalized dissipation rate, and the sensitivity of the flow on initial conditions. We show that the initial value of the ratio of the length scale of the turbulence to the mesh-size determines the precise decay of the mean-flow and the generation of the turbulent kinetic energy. We further show how a simple turbulence model can estimate the degree of nonequilibrium and inhomogeneity of grid turbulence and how this model can be extended to take into account disequilibrium observed in direct numerical simulations of decaying isotropic turbulence.

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A turbulent quarter century of active grids: from Makita (1991) to the present

Cited by 52 publications

References 87 publications

Pitfalls Measuring 1D Inertial Particle Clustering

Pitfalls Measuring 1D Inertial Particle Clustering

Energy cascades in active-grid-generated turbulent flows

Production and dissipation of kinetic energy in grid turbulence

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