The characteristic time scale for basin hydrological response using radar data

2001 ◽  
Vol 252 (1-4) ◽  
pp. 85-99 ◽  
Author(s):  
Efrat Morin ◽  
Yehouda Enzel ◽  
Uri Shamir ◽  
Rami Garti
Keyword(s):  
Time Scale ◽  
Characteristic Time ◽  
Radar Data ◽  
Characteristic Time Scale ◽  
Hydrological Response

Effect of Particle Residence Time on Particle Dispersion in a Plane Mixing Layer

1993 ◽  
Vol 115 (4) ◽  
pp. 751-759 ◽  
Author(s):  
Tsuneaki Ishima ◽  
Koichi Hishida ◽  
Masanobu Maeda
Keyword(s):  
Gas Phase ◽  
Residence Time ◽  
Time Scale ◽  
Characteristic Time ◽  
Mixing Layer ◽  
Particle Dispersion ◽  
Laser Doppler Velocimeter ◽  
Characteristic Time Scale ◽  
Two Phase ◽  
Particle Residence Time

A particle dispersion has been experimentally investigated in a two-dimensional mixing layer with a large relative velocity between particle and gas-phase in order to clarify the effect of particle residence time on particle dispersion. Spherical glass particles 42, 72, and 135 μm in diameter were loaded directly into the origin of the shear layer. Particle number density and the velocities of both particle and gas phase were measured by a laser Doppler velocimeter with modified signal processing for two-phase flow. The results confirmed that the characteristic time scale of the coherent eddy apparently became equivalent to a shorter characteristic time scale due to a less residence time. The particle dispersion coefficients were well correlated to the extended Stokes number defined as the ratio of the particle relaxation time to the substantial eddy characteristic time scale which was evaluated by taking account of the particle residence time.

UNIVERSAL MULTIFRACTAL CHARACTERIZATION AND SIMULATION OF SPEECH

1992 ◽  
Vol 02 (03) ◽  
pp. 715-719
Author(s):  
CHRIS LARNDER ◽  
NICOLAS DESAULNIERS-SOUCY ◽  
SHAUN LOVEJOY ◽  
DANIEL SCHERTZER ◽  
CLAUDE BRAUN ◽  
...  
Keyword(s):  
Time Scale ◽  
Scale Invariance ◽  
Characteristic Time ◽  
Low Frequency ◽  
Characteristic Time Scale ◽  
Low Frequencies ◽  
Universal Behavior ◽  
Nonlinear Mixing

In the 1970's it was found that; for low frequencies (<10 Hz), speech is scaling: it has no characteristic time scale. Now such scale invariance is associated with multiscaling statistics, and multifractal structures. Just as Gaussian noises frequently arise because they are generically produced by sums of many independent noise processes, scaling noises have an analogous universal behavior arising from nonlinear mixing of processes. We show that low frequency speech is consistent with these ideas, and use the measured parameters to produce stochastic speech simulations which are strikingly similar to real speech.

Daytime efficiency and characteristic time scale of interplanetary electric fields penetration to equatorial latitude ionosphere

2011 ◽  
Vol 73 (11-12) ◽  
pp. 1555-1559 ◽  
Author(s):  
C.M. Denardini ◽  
H.C. Aveiro ◽  
P.D.S.C. Almeida ◽  
L.C.A. Resende ◽  
L.M. Guizelli ◽  
...  
Keyword(s):  
Electric Fields ◽  
Time Scale ◽  
Characteristic Time ◽  
Characteristic Time Scale ◽  
Equatorial Latitude

scholarly journals Radio Flux Flicker of Extragalactic Sources

1982 ◽  
Vol 97 ◽  
pp. 327-328
Author(s):  
D. S. Heeschen
Keyword(s):  
Time Scale ◽  
Characteristic Time ◽  
Characteristic Time Scale ◽  
Radio Flux ◽  
Wavelength Radiation ◽  
Compact Sources

Compact sources (compactness evidenced by flat/complex spectra) display a “flicker” in their intrinsic centimeter wavelength radiation, with an amplitude of about 2% and a characteristic time scale of a few days.

scholarly journals Does the Radio Luminosity of Pulsar Grow up in its Later Stage?

1987 ◽  
Vol 125 ◽  
pp. 50-50
Author(s):  
T. Lu ◽  
P. C. Zhu ◽  
J. S. Kui
Keyword(s):  
Time Scale ◽  
Characteristic Time ◽  
Statistical Analyses ◽  
Striking Feature ◽  
Characteristic Time Scale ◽  
Radio Luminosity ◽  
Mean Values ◽  
The Mean

In usual statistical analyses, because of diversities of proper parameters of pulsars, some interesting features might be smeared. In order to remove these diversities, we use the mean values for all quantities of pulsars, instead of values of individual pulsar, to do statistical analyses. logP/P3 - log τ and logL - logτ have been plotted, here τ P/2P and L denote the characteristic time scale and the radio luminosity of pulsars respectively. The most striking feature is that after its initial dropping to a dip at about τ∼106 yrs, the radio luminosity of pulsar appears to grow up evidently and then redrop again. This feature is difficult to be understood in usual models. However, two tentative interpretations have been given in this paper.

Role of thermal conduction in the expansion of a plasma from a vacuum interface

1988 ◽  
Vol 66 (8) ◽  
pp. 662-673 ◽  
Author(s):  
D. Parfeniuk ◽  
A. Ng ◽  
P. Celliers
Keyword(s):  
Time Scale ◽  
Thermal Conduction ◽  
Characteristic Time ◽  
Characteristic Time Scale ◽  
Rarefaction Waves ◽  
Vacuum Interface ◽  
Aluminum Plasma ◽  
Self Similar ◽  
Expansion Model ◽  
Isotropic Expansion

The effects of thermal conduction are examined for the expansion of a plasma from a vacuum interface using an analytic model based on well-known self-similar models of rarefaction waves. Conventional analysis of shock-unloading experiments uses an isotropic expansion model. However, thermal conduction introduces a characteristic time scale during which the flow is not self-similar. The significance of this time scale for experimental measurements is also discussed. The characteristic time is calculated for an aluminum plasma using theoretical equation-of-state and conductivity models.

The ‘Hurst phenomenon’ in grid turbulence

1978 ◽  
Vol 85 (3) ◽  
pp. 573-589 ◽  
Author(s):  
K. N. Helland ◽  
C. W. Van Atta
Keyword(s):  
Wind Tunnel ◽  
Time Scale ◽  
Characteristic Time ◽  
Time Interval ◽  
Characteristic Time Scale ◽  
Grid Turbulence ◽  
Mean Velocity ◽  
Statistical Tool ◽  
Rescaled Range ◽  
Rescaled Range Analysis

Measurements of the statistical property called the ‘rescaled range’ in grid-generated turbulence exhibit a Hurst coefficient H = 0·5 for 43 < UT/M < 1850, where M/U is a characteristic time scale associated with the grid size M and mean velocity U. Theory predicts that H = 0·5 for independence of two observations separated by a time interval T, and the deviation from H = 0·5 is referred to as the ‘Hurst phenomenon’. The rescaled range obtained for grid turbulence contains an initial region UT/M < 43 of large H, approaching 1·0, corresponding approximately to the usual region of a finite non-zero autocorrelation of turbulent velocity fluctuations. For UT/M > 1850 the rescaled range breaks from H = 0·5 and rises at a significantly faster rate, H = 0·7-0·8, implying a long-term dependence or possibly non-stationarity at long times. The measured autocorrelations remain indistinguishable from zero for UT/M > 20. The break in the trend H = 0·5 is probably caused by motions on scales comparable to characteristic time scales of the wind-tunnel circulation. Rescaled-range analysis is a powerful statistical tool for determining the time scale separating the grid turbulence from the background wind-tunnel motions.

Penetration electric fields: Efficiency and characteristic time scale

2007 ◽  
Vol 69 (10-11) ◽  
pp. 1135-1146 ◽  
Author(s):  
Chao-Song Huang ◽  
Stanislav Sazykin ◽  
Jorge L. Chau ◽  
Naomi Maruyama ◽  
Michael C. Kelley
Keyword(s):  
Electric Fields ◽  
Time Scale ◽  
Characteristic Time ◽  
Characteristic Time Scale

Simple expression for the bubble-time period of two clustered air guns

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. A1-A3 ◽  
Author(s):  
Daniel Barker ◽  
Martin Landrø
Keyword(s):  
Relative Error ◽  
Time Scale ◽  
Characteristic Time ◽  
Field Data ◽  
Simple Expression ◽  
Characteristic Time Scale ◽  
Simple Method ◽  
Air Gun ◽  
Time Period ◽  
Rayleigh System

We have developed a simple method of estimating the bubble-time period of clustered air guns from the bubble-time period of a single air gun of the same type and volume. This was done by deriving a characteristic time scale for the normal Rayleigh equation, and then deriving the same scale from a modified Rayleigh system for clusters. Comparing the value for clusters with the value for a single-gun, we then estimate their relative bubble period, which gives a reasonable match (less than 4% relative error in the appropriate domain) to field data.

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