Modulational interaction between drift waves and trapped ion convective cells: A paradigm for the self‐consistent interaction of large‐scale sheared flows with small‐scale fluctuations

1995 ◽  
Vol 2 (12) ◽  
pp. 4420-4431 ◽  
Author(s):  
V. B. Lebedev ◽  
P. H. Diamond ◽  
V. D. Shapiro ◽  
G. I. Soloviev
Keyword(s):  
Large Scale ◽  
The Self ◽  
Small Scale ◽  
Drift Waves ◽  
Trapped Ion ◽  
Sheared Flows ◽  
Self Consistent ◽  
Convective Cells ◽  
Modulational Interaction

scholarly journals On the structure of the self-sustaining cycle in separating and reattaching flows

2018 ◽  
Vol 857 ◽  
pp. 907-936 ◽  
Author(s):  
A. Cimarelli ◽  
A. Leonforte ◽  
D. Angeli
Keyword(s):  
Shear Layer ◽  
Rectangular Plate ◽  
Large Scale ◽  
Reverse Flow ◽  
Leading Edge ◽  
Streamwise Velocity ◽  
The Self ◽  
Small Scale ◽  
Spectral Evolution ◽  
Mean Velocity

The separating and reattaching flows and the wake of a finite rectangular plate are studied by means of direct numerical simulation data. The large amount of information provided by the numerical approach is exploited here to address the multi-scale features of the flow and to assess the self-sustaining mechanisms that form the basis of the main unsteadinesses of the flows. We first analyse the statistically dominant flow structures by means of three-dimensional spatial correlation functions. The developed flow is found to be statistically dominated by quasi-streamwise vortices and streamwise velocity streaks as a result of flow motions induced by hairpin-like structures. On the other hand, the reverse flow within the separated region is found to be characterized by spanwise vortices. We then study the spectral properties of the flow. Given the strongly inhomogeneous nature of the flow, the spectral analysis has been conducted along two selected streamtraces of the mean velocity field. This approach allows us to study the spectral evolution of the flow along its paths. Two well-separated characteristic scales are identified in the near-wall reverse flow and in the leading-edge shear layer. The first is recognized to represent trains of small-scale structures triggering the leading-edge shear layer, whereas the second is found to be related to a very large-scale phenomenon that embraces the entire flow field. A picture of the self-sustaining mechanisms of the flow is then derived. It is shown that very-large-scale fluctuations of the pressure field alternate between promoting and suppressing the reverse flow within the separation region. Driven by these large-scale dynamics, packages of small-scale motions trigger the leading-edge shear layers, which in turn created them, alternating in the top and bottom sides of the rectangular plate with a relatively long period of inversion, thus closing the self-sustaining cycle.

scholarly journals Excitation of zonal flow by the modulational instability in electron temperature gradient driven turbulence

2008 ◽  
Vol 74 (3) ◽  
pp. 381-389 ◽  
Author(s):  
Yu. A. ZALIZNYAK ◽  
A. I. YAKIMENKO ◽  
V. M. LASHKIN
Keyword(s):  
Temperature Gradient ◽  
Electron Temperature ◽  
Large Scale ◽  
Modulational Instability ◽  
Zonal Flow ◽  
Finite Amplitude ◽  
Small Scale ◽  
Drift Waves ◽  
Zonal Flows ◽  
Electron Temperature Gradient

AbstractThe generation of large-scale zonal flows by small-scale electrostatic drift waves in electron temperature gradient driven turbulence model is considered. The generation mechanism is based on the modulational instability of a finite amplitude monochromatic drift wave. The threshold and growth rate of the instability as well as the optimal spatial scale of zonal flow are obtained.

scholarly journals Generalised quasilinear approximation of the helical magnetorotational instability

2016 ◽  
Vol 82 (3) ◽  
Author(s):  
Adam Child ◽  
Rainer Hollerbach ◽  
Brad Marston ◽  
Steven Tobias
Keyword(s):  
Large Scale ◽  
Statistical Simulation ◽  
The Self ◽  
Energy Spectra ◽  
Magnetorotational Instability ◽  
Self Consistent ◽  
Small Scales ◽  
Direct Statistical Simulation ◽  
Quasilinear Approximation ◽  
Better Than

Motivated by recent advances in direct statistical simulation (DSS) of astrophysical phenomena such as out-of-equilibrium jets, we perform a direct numerical simulation (DNS) of the helical magnetorotational instability (HMRI) under the generalised quasilinear approximation (GQL). This approximation generalises the quasilinear approximation (QL) to include the self-consistent interaction of large-scale modes, interpolating between fully nonlinear DNS and QL DNS whilst still remaining formally linear in the small scales. In this paper we address whether GQL can more accurately describe low-order statistics of axisymmetric HMRI when compared with QL by performing DNS under various degrees of GQL approximation. We utilise various diagnostics, such as energy spectra in addition to first and second cumulants, for calculations performed for a range of Reynolds and Hartmann numbers (describing rotation and imposed magnetic field strength respectively). We find that GQL performs significantly better than QL in describing the statistics of the HMRI even when relatively few large-scale modes are kept in the formalism. We conclude that DSS based on GQL (GCE2) will be significantly more accurate than that based on QL (CE2).

Vorticity and mixing in Rayleigh–Taylor Boussinesq turbulence

2016 ◽  
Vol 802 ◽  
pp. 395-436 ◽  
Author(s):  
Nicolas Schneider ◽  
Serge Gauthier
Keyword(s):  
Large Scale ◽  
Mixing Layer ◽  
Boussinesq Approximation ◽  
The Self ◽  
Small Scale ◽  
Nonlinear Growth ◽  
Small Scales ◽  
Self Similar ◽  
Order Quantities ◽  
Non Gaussian

The Rayleigh–Taylor instability induced turbulence is studied under the Boussinesq approximation focusing on vorticity and mixing. A direct numerical simulation has been carried out with an auto-adaptive multidomain Chebyshev–Fourier–Fourier numerical method. The spatial resolution is increased up to $(24\times 40)\times 940^{2}=848\,M$ collocation points. The Taylor Reynolds number is $\mathit{Re}_{\unicode[STIX]{x1D706}_{zz}}\approx 142$ and a short inertial range is observed. The nonlinear growth rate of the turbulent mixing layer is found to be close to $\unicode[STIX]{x1D6FC}_{b}=0.021$. Our conclusions may be summarized as follows.(i) The simulation data are in agreement with the scalings for the pressure ($k^{-7/3}$) and the vertical mass flux ($k^{-7/3}$).(ii) Mean quantities have a self-similar behaviour, but some inhomogeneity is still present. For higher-order quantities the self-similar regime is not fully achieved.(iii) In the self-similar regime, the mean dissipation rate and the enstrophy behave as $\langle \overline{\unicode[STIX]{x1D700}}\rangle \propto t$ and $\langle \overline{\unicode[STIX]{x1D714}_{i}\,\unicode[STIX]{x1D714}_{i}}^{1/2}\rangle \propto t^{1/2}$, respectively.(iv) The large-scale velocity fluctuation probability density function (PDF) is Gaussian, while vorticity and dissipation PDFs show large departures from Gaussianity.(v) The pressure PDF exhibits strong departures from Gaussianity and is skewed. This is related to vortex coherent structures.(vi) The intermediate scales of the mixing are isotropic, while small scales remain anisotropic. This leaves open the possibility of a small-scale buoyancy. Velocity intermediate scales are also isotropic, while small scales remain anisotropic. Mixing and dynamics are therefore consistent.(vii) Properties and behaviours of vorticity and enstrophy are detailed. In particular, equations for these quantities are written down under the Boussinesq approximation.(viii) The concentration PDF is quasi-Gaussian. The vertical concentration gradient is both non-Gaussian and strongly skewed.

Asymptotic Effect of Initial and Upstream Conditions on Turbulence

2012 ◽  
Vol 134 (6) ◽  
Author(s):  
William K. George
Keyword(s):  
New York ◽  
Turbulent Flows ◽  
Coherent Structures ◽  
Large Scale ◽  
Initial Conditions ◽  
Single Point ◽  
Building Blocks ◽  
The Self ◽  
Asymptotic Independence ◽  
Small Scale

More than two decades ago the first strong experimental results appeared suggesting that turbulent flows might not be asymptotically independent of their initial (or upstream) conditions (Wygnanski et al., 1986, “On the Large-Scale Structures in Two-Dimensional Smalldeficit, Turbulent Wakes,” J. Fluid Mech., 168, pp. 31–71). And shortly thereafter the first theoretical explanations were offered as to why we came to believe something about turbulence that might not be true (George, 1989, “The Self-Preservation of Turbulent Flows and its Relation to Initial Conditions and Coherent Structures,” Advances in Turbulence, W. George and R. Arndt, eds., Hemisphere, New York, pp. 1–41). These were contrary to popular belief. It was recognized immediately that if turbulence was indeed asymptotically independent of its initial conditions, it meant that there could be no universal single point model for turbulence (George, 1989, “The Self-Preservation of Turbulent Flows and its Relation to Initial Conditions and Coherent Structures,” Advances in Turbulence, W. George and R. Arndt, eds., Hemisphere, New York, pp. 1–41; Taulbee, 1989, “Reynolds Stress Models Applied to Turbulent Jets,” Advances in Turbulence, W. George and R. Arndt, eds., Hemisphere, New York, pp. 29–73) certainly consistent with experience, but even so not easy to accept for the turbulence community. Even now the ideas of asymptotic independence still dominate most texts and teaching of turbulence. This paper reviews the substantial additional evidence - experimental, numerical and theoretical - for the asymptotic effect of initial and upstream conditions that has accumulated over the past 25 years. Also reviewed is evidence that the Kolmogorov theory for small scale turbulence is not as general as previously believed. Emphasis has been placed on the canonical turbulent flows (especially wakes, jets, and homogeneous decaying turbulence), which have been the traditional building blocks for our understanding. Some of the important outstanding issues are discussed; and implications for the future of turbulence modeling and research, especially LES and turbulence control, are also considered.

Impact of the Korea Rapid Developing Thunderstorms (K-RDT) product nudging to the convective parameterization over the Korean Peninsula

2020 ◽  
Author(s):  
Namgu Yeo ◽  
Eun-Chul Chang ◽  
Ki-Hong Min
Keyword(s):  
Heavy Rainfall ◽  
Large Scale ◽  
Deep Convection ◽  
Weather Prediction ◽  
Model System ◽  
Prediction Skill ◽  
Precipitation Forecast ◽  
Small Scale ◽  
Convective System ◽  
Convective Cells

<p>In this study, Korea Rapid Developing Thunderstorms (K-RDT) product from geostationary meteorological satellite which represents developing stage of convective cells is nudged to the Simplified Arakawa Schubert (SAS) deep convection scheme using a simple nudging technique in order to improve prediction skill of a heavy rainfall caused by mesoscale convective system over South Korea in the short-term forecast. Impact of the K-RDT information is investigated on the Global/Regional Integrated Model system (GRIMs) regional model program (RMP) system. For the selected heavy rainfall cases, the control run without nudging and two nudging experiments with different nudging period are performed. Although the simulated precipitations in the nudging experiments tend to depend on the distribution of convective cells detected in the K-RDT algorithm, the nudging experiment shows improved precipitation forecast than the control experiment. Particularly, the experiment with nudging for longer time produces better prediction skill. The results present that the small-scale convective cells from the K-RDT which are detected with a 1-km resolution have clear impacts to large-scale atmospheric fields. Therefore, it is suggested that utilizing small-scale information of convective system in the numerical weather prediction can have critical impact to improve forecast skill when the model system, which cannot properly represent sub-grid scale convections.</p>

scholarly journals Cirrus clouds triggered by radiation, a multiscale phenomenon

2010 ◽  
Vol 10 (1) ◽  
pp. 1135-1166 ◽  
Author(s):  
F. Fusina ◽  
P. Spichtinger
Keyword(s):  
Large Scale ◽  
Radiation Transfer ◽  
Ice Nucleation ◽  
Cirrus Clouds ◽  
Small Scale ◽  
Radiative Cooling ◽  
Cirrus Cloud ◽  
Microphysics Scheme ◽  
The Stability ◽  
Convective Cells

Abstract. In this study, the influence of radiative cooling and small eddies on cirrus formation is investigated. For this purpose the non-hydrostatic, anelastic model EULAG is used with a recently developed and validated ice microphysics scheme (Spichtinger and Gierens, 2009a). Additionally, we implemented a fast radiation transfer code (Fu et al., 1998). Using idealized profiles with high ice supersaturations up to 144% and weakly stable stratifications with Brunt-Vaisala frequencies down to 0.018 s−1 within a supersaturated layer, the influence of radiation on the formation of cirrus clouds is remarkable. Due to the radiative cooling at the top of the ice supersaturated layer with cooling rates down to -3.5 K/d, the stability inside the ice supersaturated layer decreases with time. During destabilization, small eddies induced by Gaussian temperature fluctuations start to grow and trigger first nucleation. These first nucleation events then induce the growth of convective cells due to the radiative destabilization. The effects of increasing the local relative humidity by cooling due to radiation and adiabatic lifting lead to the formation of a cirrus cloud with IWC up to 33 mg/m3 and mean optical depths up to 0.36. In a more stable environment, radiative cooling is not strong enough to destabilize the supersaturated layer within 8 h; no nucleation occurs in this case. Overall triggering of cirrus clouds via radiation works only if the supersaturated layer is destabilized by radiative cooling such that small eddies can grow in amplitude and finally initialize ice nucleation. Both processes on different scales, small-scale eddies and large-scale radiative cooling are necessary.

scholarly journals Invariant solutions of minimal large-scale structures in turbulent channel flow for up to 1000

2016 ◽  
Vol 802 ◽  
Author(s):  
Yongyun Hwang ◽  
Ashley P. Willis ◽  
Carlo Cossu
Keyword(s):  
Channel Flow ◽  
Large Scale ◽  
Turbulent Channel Flow ◽  
The Self ◽  
Wall Turbulence ◽  
Small Scale ◽  
Invariant Solutions ◽  
Large Scale Structures ◽  
Turbulent Statistics ◽  
Scale Structures

Understanding the origin of large-scale structures in high-Reynolds-number wall turbulence has been a central issue over a number of years. Recently, Rawat et al. (J. Fluid Mech., vol. 782, 2015, pp. 515–540) have computed invariant solutions for the large-scale structures in turbulent Couette flow at $Re_{\unicode[STIX]{x1D70F}}\simeq 128$ using an overdamped large-eddy simulation with the Smagorinsky model to account for the effect of the surrounding small-scale motions. Here, we extend this approach to Reynolds numbers an order of magnitude higher in turbulent channel flow, towards the regime where the large-scale structures in the form of very-large-scale motions (long streaky motions) and large-scale motions (short vortical structures) emerge energetically. We demonstrate that a set of invariant solutions can be computed from simulations of the self-sustaining large-scale structures in the minimal unit (domain of size $L_{x}=3.0h$ streamwise and $L_{z}=1.5h$ spanwise) with midplane reflection symmetry at least up to $Re_{\unicode[STIX]{x1D70F}}\simeq 1000$. By approximating the surrounding small scales with an artificially elevated Smagorinsky constant, a set of equilibrium states are found, labelled upper- and lower-branch according to their associated drag. It is shown that the upper-branch equilibrium state is a reasonable proxy for the spatial structure and the turbulent statistics of the self-sustaining large-scale structures.

scholarly journals Cirrus clouds triggered by radiation, a multiscale phenomenon

2010 ◽  
Vol 10 (11) ◽  
pp. 5179-5190 ◽  
Author(s):  
F. Fusina ◽  
P. Spichtinger
Keyword(s):  
Large Scale ◽  
Ice Nucleation ◽  
Cirrus Clouds ◽  
Small Scale ◽  
Radiative Cooling ◽  
Cirrus Cloud ◽  
Microphysics Scheme ◽  
Weakly Stable ◽  
The Stability ◽  
Convective Cells

Abstract. In this study, the influence of radiative cooling and small eddies on cirrus formation is investigated. For this purpose the non-hydrostatic, anelastic model EULAG is used with a recently developed and validated ice microphysics scheme (Spichtinger and Gierens, 2009a). Additionally, we implemented a fast radiative transfer code (Fu et al., 1998). Using idealized profiles with high ice supersaturations up to 144% and weakly stable stratifications with Brunt-Vaisala frequencies down to 0.0018 s−1 within a supersaturated layer, the influence of radiation on the formation of cirrus clouds is remarkable. Due to the radiative cooling at the top of the ice supersaturated layer with cooling rates down to −3.5 K/d, the stability inside the ice supersaturated layer decreases with time. During destabilization, small eddies induced by Gaussian temperature fluctuations start to grow and trigger first nucleation. These first nucleation events then induce the growth of convective cells due to the radiative destabilization. The effects of increasing the local relative humidity by cooling due to radiation and adiabatic lifting lead to the formation of a cirrus cloud with IWC up to 33 mg/m3 and mean optical depths up to 0.36. In a more stable environment, radiative cooling is not strong enough to destabilize the supersaturated layer within 8 h; no nucleation occurs in this case. Overall triggering of cirrus clouds via radiation works only if the supersaturated layer is destabilized by radiative cooling such that small eddies can grow in amplitude and finally initialize ice nucleation. Both processes on different scales, small-scale eddies and large-scale radiative cooling are necessary.

A novel semi-supervised self-training method based on resampling for Twitter fake account identification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ziming Zeng ◽  
Tingting Li ◽  
Shouqiang Sun ◽  
Jingjing Sun ◽  
Jie Yin
Keyword(s):  
Large Scale ◽  
Class Imbalance ◽  
The Self ◽  
Third Party ◽  
Small Scale ◽  
Training Method ◽  
Training Process ◽  
Data Set ◽  
Content Type ◽  
Twitter Account

PurposeTwitter fake accounts refer to bot accounts created by third-party organizations to influence public opinion, commercial propaganda or impersonate others. The effective identification of bot accounts is conducive to accurately judge the disseminated information for the public. However, in actual fake account identification, it is expensive and inefficient to manually label Twitter accounts, and the labeled data are usually unbalanced in classes. To this end, the authors propose a novel framework to solve these problems.Design/methodology/approachIn the proposed framework, the authors introduce the concept of semi-supervised self-training learning and apply it to the real Twitter account data set from Kaggle. Specifically, the authors first train the classifier in the initial small amount of labeled account data, then use the trained classifier to automatically label large-scale unlabeled account data. Next, iteratively select high confidence instances from unlabeled data to expand the labeled data. Finally, an expanded Twitter account training set is obtained. It is worth mentioning that the resampling technique is integrated into the self-training process, and the data class is balanced at the initial stage of the self-training iteration.FindingsThe proposed framework effectively improves labeling efficiency and reduces the influence of class imbalance. It shows excellent identification results on 6 different base classifiers, especially for the initial small-scale labeled Twitter accounts.Originality/valueThis paper provides novel insights in identifying Twitter fake accounts. First, the authors take the lead in introducing a self-training method to automatically label Twitter accounts from the semi-supervised background. Second, the resampling technique is integrated into the self-training process to effectively reduce the influence of class imbalance on the identification effect.

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