Local Spectral Analysis of Turbulent Flows using Wavelet Transforms

1993 ◽  
pp. 1-26 ◽  
Author(s):  
C. Basdevant ◽  
V. Perrier ◽  
T. Philipovitch ◽  
M. Do Khac
Keyword(s):  
Spectral Analysis ◽  
Turbulent Flows ◽  
Wavelet Transforms

scholarly journals WAVELET ANALYSIS CONSIDERATIONS FOR EXPERIMENTAL NONSTATIONARY FLOW PHENOMENA

2016 ◽  
Vol 15 (1) ◽  
pp. 67
Author(s):  
M. L. S. Indrusiak ◽  
A. J. Kozakevicius ◽  
S. V. Möller
Keyword(s):  
Turbulent Flows ◽  
Wavelet Transforms ◽  
Fourier Spectrum ◽  
Transient Flow ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Velocity Measurements ◽  
Discrete Wavelet Packet Transform ◽  
Flow Phenomena ◽  
The Fourier Transform

In this work, wavelet transforms are the analysis tools for studying transient and discontinuous phenomena associated to turbulent flows. The application in quest results from velocity measurements with hot wire anemometry in the transient wake considering a circular cylinder in an aerodynamic channel. Continuous and discrete wavelet transforms are applied and compared with the corresponding results given by the Fourier transform. For the continuous wavelet transform, the Morlet function was adopted as transform basis, and for the discrete case, the Daubechies orthonormal wavelet with 20 null moments. Results using the discrete wavelet packet transform are also presented and compared. A wake past a cylinder was analytically simulated and compared with the actual one, both in transient flow. The ability of the wavelet transforms in the analysis of unsteady phenomena and the potential of the wavelet approach as a complementary tool to the Fourier spectrum for the analysis of stationary phenomena is presented and discussed.

Orthonormal wavelet decomposition of turbulent flows: intermittency and coherent structures

1997 ◽  
Vol 348 ◽  
pp. 177-199 ◽  
Author(s):  
R. CAMUSSI ◽  
G. GUJ
Keyword(s):  
Turbulent Flows ◽  
Coherent Structures ◽  
Velocity Component ◽  
Wavelet Transforms ◽  
Wavelet Decomposition ◽  
Transverse Velocity ◽  
Mixing Layer ◽  
Grid Turbulence ◽  
Jet Mixing ◽  
Vortex Tubes

Experimental data obtained in various turbulent flows are analysed by means of orthogonal wavelet transforms. Several configurations are analysed: homogeneous grid turbulence at low and very low Reλ, and fully developed jet turbulence at moderate and high Reλ. It is shown by means of the wavelet decomposition in combination with the form of scaling named extended self-similarity that some statistical properties of fully developed turbulence may be extended to low-Reλ flows. Indeed, universal properties related to intermittency are observed down to Reλ≃10. Furthermore, the use of a new conditional averaging technique of velocity signals, based on the wavelet transform, permits the identification of the time signatures of coherent structures which may or may not be responsible for intermittency depending on the scale of the structure itself. It is shown that in grid turbulence, intermittency at the smallest scales is related to structures with small characteristic size and with a shape that may be related to the passage of vortex tubes. In jet turbulence, the longitudinal velocity component reveals that intermittency may be induced by structures with a size of the order of the integral length. This effect is interpreted as the signature of the characteristic jet mixing layer structures. The structures identified on the transverse velocity component of the jet case turn out on the other hand not to be affected by the mixing layer and the corresponding shape is again correlated with the signature of vortex tubes.

Time-Local Spectral Analysis for Non-Stationary Time Series: The S-Transform for Noisy Signals

2003 ◽  
Vol 03 (03) ◽  
pp. L357-L364 ◽  
Author(s):  
C. R. Pinnegar ◽  
L. Mansinha
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transforms ◽  
Gaussian White Noise ◽  
Stationary Time Series ◽  
Time Frequency ◽  
S Transform ◽  
Undesirable Characteristic ◽  
Noisy Signals ◽  
Short Time

The S-transform is a method of time-local spectral analysis (also known as time-frequency analysis), a modified short-time Fourier Transform, in which the width of the analyzing window scales inversely with frequency, in analogy with continuous wavelet transforms. If the time series is non-stationary and consists of a mix of Gaussian white noise and a deterministic signal, though, this type of scaling leads to larger apparent noise amplitudes at higher frequencies. In this paper, we introduce a modified S-transform window with a different scaling function that addresses this undesirable characteristic.

scholarly journals USING WAVELETS IN ECONOMICS. AN APPLICATION ON THE ANALYSIS OF WAGE-PRICE RELATION

2017 ◽  
Vol 2 (1) ◽  
pp. 32-42
Author(s):  
Vasile-Aurel Caus ◽  
Daniel Badulescu ◽  
Mircea Cristian Gherman
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transforms ◽  
Wavelet Decomposition ◽  
Fourier Transforms ◽  
Nominal Wage ◽  
Economic Issues ◽  
Wavelet Methods ◽  
Theoretical Considerations ◽  
Analysis Of Time Series

In the last decades, more and more approaches of economic issues have used mathematical tools, and among the most recent ones, spectral and wavelet methods are to be distinguished. If in the case of spectral analysis the approaches and results are sufficiently clear, while the use of wavelet decomposition, especially in the analysis of time series, is not fully valorized. The purpose of this paper is to emphasize how these methods are useful for time series analysis. After theoretical considerations on Fourier transforms versus wavelet transforms, we have presented the methodology we have used and the results obtained by using wavelets in the analysis of wage-price relation, based on some empirical data. The data we have used is concerning the Romanian economy - the inflation and the average nominal wage denominated in US dollars, between January 1991 and May 2016, highlighting that the relation between nominal salary and prices can be revealed more accurately by use of wavelets

Sign in / Sign up

Export Citation Format

Share Document