Intermittency and Reynolds number

Kahalerras, H.; Malécot, Y.; Gagne, Yves; Castaing, B.

doi:10.1063/1.869613

Cited by 61 publications

(61 citation statements)

References 20 publications

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“…The functions f and g are again dimensionless. The conditions for (16) and (17) to solve (12) are (see [12] for the solution method)…”

Section: Spectral Energy Budget In the Decay Regionmentioning

confidence: 99%

“…The estimated frequency response of this anemometry system is 1.5 to 9 times higher than the Kolmogorov frequency f η = U 2πη . The spatially-varying longitudinal velocity componentũ(x) in the direction of the mean flow was recovered from the time-varying velocityũ(t) measured with the hot-wire probe by means of local Taylor's hypothesis as defined in [16].…”

Section: Velocity Measurementsmentioning

confidence: 99%

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Turbulence without Richardson–Kolmogorov cascade

Mazellier¹,

Vassilicos²

2010

Physics of Fluids

200

305

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We investigate experimentally wind tunnel turbulence generated by multiscale/fractal grids pertaining to the same class of low-blockage space-filling fractal square grids. These grids are not active and nevertheless produce very much higher turbulence intensities u ′ /U and Reynolds numbers Re λ than higher blockage regular grids. Our hot wire anemometry confirms the existence of a protracted production region where turbulence intensity grows followed by a decay region where it decreases, as first reported by Hurst & Vassilicos [15]. We introduce the wake-interaction length-scale x * and show that the peak of turbulence intensity demarcating these two regions along the centreline is positioned at about 0.5x * . The streamwise evolutions on the centreline of the streamwise mean flow and of various statistics of the streamwise fluctuating velocity all scale with x * . Mean flow and turbulence intensity profiles are inhomogeneous at streamwise distances from the fractal grid smaller than 0.5x * , but appear quite homogeneous beyond 0.5x * . The velocity fluctuations are highly non-gaussian in the production region but approximately gaussian in the decay region. Our results confirm the finding of Seoud & Vassilicos [31] that the ratio of the integral length-scale L u to the Taylor microscale λ remains constant even though the Reynolds number Re λ decreases during turbulence decay in the region beyond 0.5x * . As a result the scaling L u /λ ∼ Re λ which follows from the u ′3 /L u scaling of the dissipation rate in boundary-free shear flows and in usual grid-generated turbulence does not hold here. This extraordinary decoupling is consistent with a non-cascading and instead self-preserving single-length scale type of decaying homogeneous turbulence proposed by George & Wang [12], but we also show that L u /λ is nevertheless an increasing function of the inlet Reynolds number Re 0 . Finally, we offer a detailed comparison of the main assumption and consequences of the George & Wang theory against our fractal-generated turbulence data.

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“…The functions f and g are again dimensionless. The conditions for (16) and (17) to solve (12) are (see [12] for the solution method)…”

Section: Spectral Energy Budget In the Decay Regionmentioning

confidence: 99%

Section: Velocity Measurementsmentioning

confidence: 99%

Turbulence without Richardson–Kolmogorov cascade

Mazellier¹,

Vassilicos²

2010

Physics of Fluids

200

305

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“…For further details on this data set and past ONERA S1 wind tunnel experiments, please refer to Refs. [81][82][83]. we utilize them to evaluate the strengths (weaknesses) of the proposed MLE-based structure function estimation approach.…”

Section: Wind Tunnel Datamentioning

confidence: 99%

Estimating higher-order structure functions from geophysical turbulence time series: Confronting the curse of the limited sample size

DeMarco

Basu

2017

Phys. Rev. E

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Utilizing synthetically generated random variates and laboratory measurements, we document the inherent limitations of the conventional structure function approach in limited sample size settings. We demonstrate that an alternative approach, based on the principle of maximum likelihood, can provide nearly unbiased structure function estimates with far less uncertainty under such unfavorable conditions. The superiority of this approach over the conventional approach does not diminish even in the case of strongly correlated samples. Two entirely different types of probability distributions, which have been reported in the turbulence literature, are found to be compatible with the proposed approach.

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“…-The data we are analysing is a temporal measurement (sampled at frequency f s =25 kHz) of velocity in a grid turbulence experiment in the ONERA wind tunnel in Modane [19]. The Taylor-scale based Reynolds number is about Re = 2700 and the turbulence rate is about 8%; the Kolmogorov k −5/3 law for the energy spectrum holds on an inertial range of approximately three time decades (Figure 1(a)).…”

Section: Information Theorymentioning

confidence: 99%

“…We checked that our estimation of the entropy rate does not depend on the number of neighbors used in the k-nn search algorithm (k = 5 and 10) nor on the sample size N (N = 2 16 , 2 17 and 2 19 ). We measured that for a fixed set (k, N ) the standard deviation of the entropy rate is much smaller than the standard deviations of H(X) and I(X τ , X (m,τ ) ) considered separately.…”

Section: Robustnessmentioning

confidence: 99%

Scaling of information in turbulence

Granero-Belinchon¹,

Roux²,

Garnier³

2016

EPL

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Cf -Entropy in information theory PACS 47.27.Jv -High-Reynolds-number turbulence PACS 89.75.Da -Scaling phenomena in complex systems Abstract -We propose a new perspective on Turbulence using Information Theory. We compute the entropy rate of a turbulent velocity signal and we particularly focus on its dependence on the scale. We first report how the entropy rate is able to describe the distribution of information amongst scales, and how one can use it to isolate the injection, inertial and dissipative ranges, in perfect agreement with the Batchelor model and with a fractional Brownian motion (fBM) model. We provide analytical derivations of the entropy rate scalings in these two models. In a second stage, we design a conditioning procedure in order to finely probe the asymmetries in the statistics that are responsible for the energy cascade. Our approach is very generic and can be applied to any multiscale complex system.

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Intermittency and Reynolds number

Cited by 61 publications

References 20 publications

Turbulence without Richardson–Kolmogorov cascade

Turbulence without Richardson–Kolmogorov cascade

Estimating higher-order structure functions from geophysical turbulence time series: Confronting the curse of the limited sample size

Scaling of information in turbulence

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