Improved ε expansion in theory of turbulence: Calculation of Kolmogorov constant and skewness factor

Chaos Theory ◽  
2011 ◽  
Author(s):  
L. Ts. Adzhemyan ◽  
M. Hnatich ◽  
J. Honkonen
Keyword(s):  
Skewness Factor ◽  
Kolmogorov Constant

Numerical study of isotropic ocean swell

2019 ◽  
Vol 489 (5) ◽  
pp. 512-516
Author(s):  
V. V. Geogjaev ◽  
V. E. Zakharov ◽  
S. I. Badulin
Keyword(s):  
Numerical Study ◽  
Short Time ◽  
Kolmogorov Constant

A new algorithm is used for detailed numerical study of the evolution of isotropic swell in a homogeneous ocean. It is shown that the Zakharov-Filonenko spectrum occurs in an explosive manner in a short time. The Kolmogorov constant of the solution is estimated numerically.

scholarly journals Heterogeneous Beliefs and Risk-Neutral Skewness

2012 ◽  
Vol 47 (4) ◽  
pp. 851-872 ◽  
Author(s):  
Geoffrey C. Friesen ◽  
Yi Zhang ◽  
Thomas S. Zorn
Keyword(s):  
Factor Analysis ◽  
Latent Variables ◽  
Stock Options ◽  
Systematic Risk ◽  
Heterogeneous Beliefs ◽  
Firm Level ◽  
Cross Sectional ◽  
Skewness Factor ◽  
Risk Neutral ◽  
Using Data

AbstractThis study tests whether belief differences affect the cross-sectional variation of risk-neutral skewness using data on firm-level stock options traded on the Chicago Board Options Exchange from 2003 to 2006. We find that stocks with greater belief differences have more negative skews, even after controlling for systematic risk and other firm-level variables known to affect skewness. Factor analysis identifies latent variables linked to risk and belief differences. The belief factor explains more variation in the risk-neutral skewness than the risk-based factor. Our results suggest that belief differences may be one of the unexplained firm-specific components affecting skewness.

KOLMOGOROV CONSTANT IN LARGE SPACE DIMENSIONS

2002 ◽  
Vol 16 (32) ◽  
pp. 4839-4845 ◽  
Author(s):  
MALAY K. NANDY
Keyword(s):  
Eddy Viscosity ◽  
Energy Equation ◽  
Space Dimension ◽  
Direct Interaction ◽  
Large Space ◽  
Distant Interaction ◽  
Direct Interaction Approximation ◽  
Dimension Expansion ◽  
Kolmogorov Constant ◽  
Interaction Approximation

A large d (space dimension) expansion together with the ∊-expansion is implemented to calculate the Kolmogorov constant from the energy equation of Kraichnan's direct-interaction approximation using the Heisenberg's eddy-viscosity approximation and Kraichnan's distant-interaction algorithm. The Kolmogorov constant C is found to be C = C0 d1/3 in the leading order of a 1/d expansion. This is consistent with Fournier, Frisch, and Rose. The constant C0 evaluated in the above scheme, is found to be C0 = (16/27)1/3.

On the near-wall characteristics of acceleration in turbulence

2010 ◽  
Vol 659 ◽  
pp. 405-419 ◽  
Author(s):  
K. YEO ◽  
B.-G. KIM ◽  
C. LEE
Keyword(s):  
Turbulent Flows ◽  
Streamwise Vortex ◽  
Reynolds Numbers ◽  
Viscous Sublayer ◽  
Centripetal Acceleration ◽  
Vortical Structures ◽  
Turbulent Channel Flows ◽  
Background Shear ◽  
Kolmogorov Constant ◽  
Near Wall

The behaviour of fluid-particle acceleration in near-wall turbulent flows is investigated in numerically simulated turbulent channel flows at low to moderate Reynolds numbers, Reτ = 180~600). The acceleration is decomposed into pressure-gradient (irrotational) and viscous contributions (solenoidal acceleration) and the statistics of each component are analysed. In near-wall turbulent flows, the probability density function of acceleration is strongly dependent on the distance from the wall. Unexpectedly, the intermittency of acceleration is strongest in the viscous sublayer, where the acceleration flatness factor of O(100) is observed. It is shown that the centripetal acceleration around coherent vortical structures is an important source of the acceleration intermittency. We found sheet-like structures of strong solenoidal accelerations near the wall, which are associated with the background shear modified by the interaction between a streamwise vortex and the wall. We found that the acceleration Kolmogorov constant is a linear function of y+ in the log layer. The Reynolds number dependence of the acceleration statistics is investigated.

scholarly journals Velocity-Based EDR Retrieval Techniques Applied to Doppler Radar Measurements from Rain: Two Case Studies

2019 ◽  
Vol 36 (9) ◽  
pp. 1693-1711 ◽  
Author(s):  
A. C. P. Oude Nijhuis ◽  
C. M. H. Unal ◽  
O. A. Krasnov ◽  
H. W. J. Russchenberg ◽  
A. G. Yarovoy
Keyword(s):  
Case Studies ◽  
Doppler Radar ◽  
Energy Dissipation Rate ◽  
Accurate Estimation ◽  
Retrieval Technique ◽  
Sonic Anemometers ◽  
Radar Measurements ◽  
Rain Events ◽  
Kolmogorov Constant

In this article, five velocity-based energy dissipation rate (EDR) retrieval techniques are assessed. The EDR retrieval techniques are applied to Doppler measurements from Transportable Atmospheric Radar (TARA)—a precipitation profiling radar—operating in the vertically fixed-pointing mode. A generalized formula for the Kolmogorov constant is derived, which gives potential for the application of the EDR retrieval techniques to any radar line of sight (LOS). Two case studies are discussed that contain rain events of about 2 and 18 h, respectively. The EDR values retrieved from the radar are compared to in situ EDR values from collocated sonic anemometers. For the two case studies, a correlation coefficient of 0.79 was found for the wind speed variance (WSV) EDR retrieval technique, which uses 3D wind vectors as input and has a total sampling time of 10 min. From this comparison it is concluded that the radar is able to measure EDR with a reasonable accuracy. Almost no correlation was found for the vertical wind velocity variance (VWVV) EDR retrieval technique, as it was not possible to sufficiently separate the turbulence dynamics contribution to the radar Doppler mean velocities from the velocity contribution of falling raindrops. An important cause of the discrepancies between radar and in situ EDR values is thus due to insufficient accurate estimation of vertical air velocities.

Turbulent Flow Through Spacer Grids in Rod Bundles

1998 ◽  
Vol 120 (4) ◽  
pp. 786-791 ◽  
Author(s):  
Sun Kyu Yang ◽  
Moon Ki Chung
Keyword(s):  
Turbulent Flow ◽  
Laser Doppler Velocimetry ◽  
Hydraulic Characteristics ◽  
Rod Bundles ◽  
Spacer Grid ◽  
Spacer Grids ◽  
Rod Bundle ◽  
Turbulence Decay ◽  
Skewness Factor ◽  
Flow Through

The effects of the spacer grids with mixing vanes in rod bundles on the turbulent structure were investigated experimentally. The detailed hydraulic characteristics in subchannels of a 5 × 5 rod bundle with mixing spacer grids were measured upstream and downstream of the spacer grid by using a one component LDV (Laser Doppler Velocimetry). Axial velocity and turbulent intensity, skewness factor, and flatness factor were measured. The turbulence decay behind spacer grids was obtained from measured data. The trend of turbulence decay behaves in a similar way as turbulent flow through mesh grids or screens. Pressure drop measurements were also performed to evaluate the loss coefficient for the spacer grid and the friction factor for a rod bundle.

A Turbulent Model for Gas-Particle Jets

2000 ◽  
Vol 122 (3) ◽  
pp. 505-509 ◽  
Author(s):  
J. Garcı´a ◽  
A. Crespo
Keyword(s):  
Mixing Layer ◽  
Stokes Number ◽  
Particle Dispersion ◽  
Lagrangian Model ◽  
Grid Turbulence ◽  
Mean Velocity ◽  
Dissipation Term ◽  
Particle Flows ◽  
Integral Scales ◽  
Kolmogorov Constant

This work is concerned with turbulent diffusion in gas-particle flows. The cases studied correspond to dilute flows and small Stokes number, this implies that the mean velocity of the particles is very similar to that of the fluid element. The classical k-ε method is used to model the gas-phase, modified with additional terms for the k and ε equations, that takes into account the effect of particles on the carrier phase. The additional dissipation term included in the equation for k is due to the slip between phases at an intermediate scale, far from both the Kolmogorov and the integral scales. This term has a proportionality constant equal to 3/2 of Kolmogorov constant, C0. In this paper, a value of 3.0 has been used for this constant as suggested by Du et al., 1995, “Estimation of the Kolmogorov Constant C0 for the Langarian Structure Using a Second-Order Lagrangian Model of Grid Turbulence,” Phys. Fluids 7, (12), pp. 3083–3090. The additional source term for the ε equation is taken as proportional to ε/k, as is usually done. In all experiments analyzed the particles increased the dissipation of turbulent kinetic energy. A comparison is made between the results obtained with the model proposed in this work and the experiments of Shuen et al., 1985, “Structure of Particle-Laden Jets: Measurements and Predictions,” AIAA Journal, 23, No. 3, and Hishida et al., 1992, “Experiments on Particle Dispersion in a Turbulent Mixing Layer,” ASME Journal of Fluids Engineering, 119, pp. 181–194. [S0098-2202(00)02103-9]

A New Approach to Turbulence

1997 ◽  
Vol 12 (18) ◽  
pp. 3121-3152 ◽  
Author(s):  
V. M. Canuto ◽  
M. S. Dubovikov
Keyword(s):  
Present Model ◽  
Large Strain ◽  
General Structure ◽  
Nonlinear Interactions ◽  
Closed Set ◽  
New Approach ◽  
Overall Performance ◽  
Sweeping Effect ◽  
Shear Driven Flows ◽  
Kolmogorov Constant

We propose a closed set of dynamic equations to describe turbulence. The equations are the result of systematic and heuristic elements. Specifically, the UV part of the nonlinear interactions, represented by a dynamical viscosity, is computed for a stirring force of a particular nature. However, since the results exhibit a general structure, we suggest heuristically to extend them to arbitrary flows. Because of nonrenormalizable divergences, the IR part of the nonlinear interactions has constituted a serious problem. We suggest a heuristic model, the basic ingredient of which is that the transfer of energy among eddies is mostly a local process. We show that possible adjustable parameters are actually fixed by the model itself. Because of the heuristic nature of one part of the model, its overall validity rests largely on the accumulated evidence gathered from checking its predictions against data from a wide variety of flows. The model has been tested against more than seventy turbulence statistics for homogeneous isotropic and anisotropic flows (the Kolmogorov constant is predicted to be Ko = 5/3). The overall performance is good. Here, we first extend the model to inhomogeneous flows and test the predictions using the newest laboratory and DNS data on turbulent convection at large Ra (Rayleigh number). The model reproduces both types of data quite accurately. Second, we study the problem of the so-called "sweeping effect" and derive the relation between the ω and k-spectra. Third, we show that for shear driven flows the present model reproduces well the data at large strain rates while the widely used K - ∊ model does not.

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