Passive Scalar Intermittency in Low Temperature Helium Flows

Moisy, Frédéric; Willaime, Hervé; Andersen, Jacob Sparre; Tabeling, Patrick

doi:10.1103/physrevlett.86.4827

Cited by 70 publications

(70 citation statements)

References 16 publications

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“…This can be readily understood in the framework of the multifractal model, where 1 would be the codimension of these structures [15]. A similar behavior of the scaling exponents both in numerical simulations [12] and in experiments [10] has been found for the concentration fluctuations of a passive scalar that is advected by a turbulent flow. It has been linked to the well-known ''ramp and cliff '' structure of the concentration field [4,12].…”

Section: P H Y S I C a L R E V I E W L E T T E R S Week Endingmentioning

confidence: 58%

Small Scale Velocity Jumps in Shear Turbulence

Staicu

Water

2003

Phys. Rev. Lett.

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Section: P H Y S I C a L R E V I E W L E T T E R S Week Endingmentioning

confidence: 58%

Small Scale Velocity Jumps in Shear Turbulence

Staicu

Water

2003

Phys. Rev. Lett.

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“…A consequence of this saturation is a constant ratio of the far tails of the PDFs for inertial range separations 42,46,47 . The ratio of two PDFs, for separations r 1 = 43η and r 2 = 113η well into the inertial range, is plotted in Fig.…”

Section: Spatial and Temporal Intermittencymentioning

confidence: 99%

“…For temperature increments |δθ r | > ∼ 3θ rms , this ratio indeed tends as expected towards a constant. This saturation is a feature shared by several scalar turbulent systems, e.g., for passive scalar in Navier-Stokes turbulence 46,47 and for active scalar in buoyancy-driven turbulence 42,48 . Especially, the present obtained ξ r ∞ agrees well with the value of 0.8 found in turbulent RB convection 42,48 .…”

Section: Spatial and Temporal Intermittencymentioning

confidence: 99%

Temporal evolution and scaling of mixing in two-dimensional Rayleigh-Taylor turbulence

Zhou

2013

Physics of Fluids

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We report a high-resolution numerical study of two-dimensional (2D) miscible Rayleigh-Taylor (RT) incompressible turbulence with the Boussinesq approximation. An ensemble of 100 independent realizations were performed at small Atwood number and unit Prandtl number with a spatial resolution of 2048 × 8193 grid points. Our main focus is on the temporal evolution and the scaling behavior of global quantities and of small-scale turbulence properties. Our results show that the buoyancy force balances the inertial force at all scales below the integral length scale and thus validate the basic force-balance assumption of the Bolgiano-Obukhov scenario in 2D RT turbulence. It is further found that the Kolmogorov dissipation scale η(t) ∼ t 1/8 , the kinetic-energy dissipation rate ε u (t) ∼ t −1/2 , and the thermal dissipation rate ε θ (t) ∼ t −1 . All of these scaling properties are in excellent agreement with the theoretical predictions of the Chertkov model [Phys. Rev. Lett. 91, 115001 (2003)]. We further discuss the emergence of intermittency and anomalous scaling for high order moments of velocity and temperature differences. The scaling exponents ξ r p of the pth-order temperature structure functions are shown to saturate to ξ r ∞ ≃ 0.78 ± 0.15 for the highest orders, p ∼ 10. The value of ξ r ∞ and the order at which saturation occurs are compatible with those of turbulent Rayleigh-Bénard (RB) convection [Phys. Rev. Lett. 88, 054503 (2002)], supporting the scenario of universality of buoyancy-driven turbulence with respect to the different boundary conditions characterizing the RT and RB systems.

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“…Belmonte and Libchaber [3] used the skewness of the temperature derivative as a signature of the plumes. Zhou and Xia [4] associated the difference in the skewness of the positive and negative parts of the temperature difference with the presence of plumes and identified the plumes whenever the temperature difference becomes larger than a chosen threshold [6]. In Ref.…”

mentioning

confidence: 99%

Extraction of Plumes in Turbulent Thermal Convection

et al. 2004

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We present a scheme to extract information about plumes, a prominent coherent structure in turbulent thermal convection, from simultaneous local velocity and temperature measurements. Using this scheme, we study the temperature dependence of the plume velocity and understand the results using the equations of motion. We further obtain the average local heat flux in the vertical direction at the cell center. Our result shows that heat is not mainly transported through the central region but instead through the regions near the sidewalls of the convection cell. where v is the velocity field, p the pressure divided by density, T the temperature field, andẑ is the unit vector in the vertical direction. Furthermore, δT = T − T 0 where T 0 is the mean temperature of the bulk fluid, g is the acceleration due to gravity and α, ν, and κ are respectively the volume expansion coefficient, kinematic viscosity and thermal diffusivity of the fluid. The state of fluid motion is characterized by the geometry of the cell and two dimensionless parameters: the Rayleigh number, Ra = αg∆L 3 /(νκ), which measures how much the fluid is driven and the Prandtl number, Pr = ν/κ, which is the ratio of the diffusivities of momentum and heat of the fluid. Here ∆ is the maintained temperature difference between the bottom and the top, and L is the height of the cell. When Ra is sufficiently large, the convective motion becomes turbulent. In turbulent convection, local velocity and temperature measurements taken at a point within the convection cell display complex fluctuations in time. On the other hand, visualization of the flow reveals recurring coherent structures. One prominent coherent structure is a plume, which is a mushroom-like flow generated by buoyancy. Thus at least two strategies can be employed to study turbulent thermal convection or turbulent flows in general. One is to analyze and understand the fluctuations of the local measurements. The other is to characterize the coherent structures and study and understand their dynamics. These two approaches are not independent but provide complementary knowledge of turbulent flows. In particular, there is the natural question of whether and how information about the coherent structures can be extracted from the local measurements.For turbulent flows not driven by buoyancy, various methods including proper orthogonal decomposition, conditional sampling and wavelet analysis have been proposed to identify coherent vortical structures from local velocity measurements [2]. On the other hand, much less work has been done in identifying plumes or extracting information about plumes in turbulent thermal convection [3,4,5]. Belmonte and Libchaber[3] used the skewness of the temperature derivative as a signature of the plumes. Zhou and Xia[4] associated the difference in the skewness of the positive and negative parts of the temperature difference with the presence of plumes and identified the plumes whenever the temperature difference becomes larger than a chosen threshold [6]. In Ref.[5], plu...

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Passive Scalar Intermittency in Low Temperature Helium Flows

Cited by 70 publications

References 16 publications

Small Scale Velocity Jumps in Shear Turbulence

Small Scale Velocity Jumps in Shear Turbulence

Temporal evolution and scaling of mixing in two-dimensional Rayleigh-Taylor turbulence

Extraction of Plumes in Turbulent Thermal Convection

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