Vorticity filaments in two-dimensional turbulence: creation, stability and effect

1997 ◽  
Vol 346 ◽  
pp. 49-76 ◽  
Author(s):  
N. K.-R. KEVLAHAN ◽  
M. FARGE
Keyword(s):  
Turbulent Flows ◽  
Vortex Core ◽  
Wavelet Packet ◽  
Energy Spectra ◽  
Wavelet Packet Decomposition ◽  
Two Dimensional ◽  
Newtonian Viscosity ◽  
Vortex Centre ◽  
Coherent Vortex ◽  
The Difference

Vorticity filaments are characteristic structures of two-dimensional turbulence. The formation, persistence and effect of vorticity filaments are examined using a high-resolution direct numerical simulation (DNS) of the merging of two positive Gaussian vortices pushed together by a weaker negative vortex. Many intense spiral vorticity filaments are created during this interaction and it is shown using a wavelet packet decomposition that, as has been suggested, the coherent vortex stabilizes the filaments. This result is confirmed by a linear stability analysis at the edge of the vortex and by a calculation of the straining induced by the spiral structure of the filament in the vortex core. The time-averaged energy spectra for simulations using hyper-viscosity and Newtonian viscosity have slopes of −3 and −4 respectively. Apart from a much higher effective Reynolds number (which accounts for the difference in energy spectra), the hyper-viscous simulation has the same dynamics as the Newtonian viscosity simulation. A wavelet packet decomposition of the hyper-viscous simulation reveals that after the merger the energy spectra of the filamentary and coherent parts of the vorticity field have slopes of −2 and −6 respectively. An asymptotic analysis and DNS for weak external strain shows that a circular filament at a distance R from the vortex centre always reduces the deformation of a Lamb's (Gaussian) vortex in the region r[ges ]R. In the region r<R the deformation is also reduced provided the filament is intense and is in the vortex core, otherwise the filament may slightly increase the deformation. The results presented here should be useful for modelling the coherent and incoherent parts of two-dimensional turbulent flows.

Energy spectra of steady two-dimensional turbulent flows

2000 ◽  
Vol 61 (6) ◽  
pp. 6572-6577 ◽  
Author(s):  
Norbert Schorghofer
Keyword(s):  
Turbulent Flows ◽  
Energy Spectra ◽  
Two Dimensional

Improved predictability of two-dimensional turbulent flows using wavelet packet compression

1992 ◽  
Vol 10 (4-6) ◽  
pp. 229-250 ◽  
Author(s):  
Marie Farge ◽  
Eric Goirand ◽  
Yves Meyer ◽  
Frédéric Pascal ◽  
Mladen Victor Wickerhauser
Keyword(s):  
Turbulent Flows ◽  
Wavelet Packet ◽  
Two Dimensional

Study on Fractal Box Dimension and Its Application to Fault Diagnosis of Hydraulic Pump

2012 ◽  
Vol 490-495 ◽  
pp. 1526-1530
Author(s):  
Xi Liang Liu ◽  
Gui Ming Chen ◽  
Fang Xi Li
Keyword(s):  
Wavelet Packet ◽  
Vibration Signal ◽  
Box Dimension ◽  
Wavelet Packet Decomposition ◽  
Hydraulic Pump ◽  
Self Similarity ◽  
The Difference ◽  
Fault Mechanism ◽  
Box Dimensions ◽  
Fault Characteristic

Fractal box dimension is a new method to represent self-similarity and complexity of non-stationary mechanical fault signal. In this paper, we first present the basic conception of fractal and box dimension. Then we propose the algorithm to calculate the box dimension of discrete signal. On the basis of wavelet packet decomposition, we quantitatively analyze the vibration signal under different conditions with fault characteristic by way of box dimension. Experimental results show that the box dimensions are different in evidence because of the difference of fault mechanism. As a successful application, fractal box dimension can be used to recognize the fault pattern of hydraulic pump effectively.

Extended two-dimensional belief function based on divergence measurement

2020 ◽  
pp. 1-8
Author(s):  
Jianping Fan ◽  
Jing Wang ◽  
Meiqin Wu
Keyword(s):  
Belief Function ◽  
Distribution Functions ◽  
Divergence Measure ◽  
Uncertain Information ◽  
Belief Functions ◽  
Two Dimensional ◽  
Probability Distribution Functions ◽  
Basic Probability ◽  
The Difference ◽  
Supporting Degree

The two-dimensional belief function (TDBF = (mA, mB)) uses a pair of ordered basic probability distribution functions to describe and process uncertain information. Among them, mB includes support degree, non-support degree and reliability unmeasured degree of mA. So it is more abundant and reasonable than the traditional discount coefficient and expresses the evaluation value of experts. However, only considering that the expert’s assessment is single and one-sided, we also need to consider the influence between the belief function itself. The difference in belief function can measure the difference between two belief functions, based on which the supporting degree, non-supporting degree and unmeasured degree of reliability of the evidence are calculated. Based on the divergence measure of belief function, this paper proposes an extended two-dimensional belief function, which can solve some evidence conflict problems and is more objective and better solve a class of problems that TDBF cannot handle. Finally, numerical examples illustrate its effectiveness and rationality.

scholarly journals Unfolding energy spectra of double-periodicity two-dimensional systems: Twisted bilayer graphene and MoS2 on graphene

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Yu-ichiro Matsushita ◽  
Hirofumi Nishi ◽  
Jun-ichi Iwata ◽  
Taichi Kosugi ◽  
Atsushi Oshiyama
Keyword(s):  
Bilayer Graphene ◽  
Energy Spectra ◽  
Two Dimensional ◽  
Twisted Bilayer Graphene

scholarly journals Examining the Applicability of Wavelet Packet Decomposition on Different Forecasting Models in Annual Rainfall Prediction

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 1997
Author(s):  
Hua Wang ◽  
Wenchuan Wang ◽  
Yujin Du ◽  
Dongmei Xu
Keyword(s):  
Wavelet Packet ◽  
Moving Average ◽  
Back Propagation ◽  
Processing Technique ◽  
Annual Rainfall ◽  
Wavelet Packet Decomposition ◽  
Rainfall Forecasting ◽  
Promising Alternative ◽  
Forecasting Models ◽  
Precipitation Prediction

Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action. This paper examines the applicability of several forecasting models based on wavelet packet decomposition (WPD) in annual rainfall forecasting, and a novel hybrid precipitation prediction framework (WPD-ELM) is proposed coupling extreme learning machine (ELM) and WPD. The works of this paper can be described as follows: (a) WPD is used to decompose the original precipitation data into several sub-layers; (b) ELM model, autoregressive integrated moving average model (ARIMA), and back-propagation neural network (BPNN) are employed to realize the forecasting computation for the decomposed series; (c) the results are integrated to attain the final prediction. Four evaluation indexes (RMSE, MAE, R, and NSEC) are adopted to assess the performance of the models. The results indicate that the WPD-ELM model outperforms other models used in this paper and WPD can significantly enhance the performance of forecasting models. In conclusion, WPD-ELM can be a promising alternative for annual precipitation forecasting and WPD is an effective data pre-processing technique in producing convincing forecasting models.

scholarly journals Visual Simulation of Detailed Turbulent Water by Preserving the Thin Sheets of Fluid

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 502 ◽  
Author(s):  
Jong-Hyun Kim ◽  
Wook Kim ◽  
Young Kim ◽  
Jung Lee
Keyword(s):  
Turbulent Flows ◽  
Visual Simulation ◽  
Numerical Dissipation ◽  
Small Scale ◽  
Thin Sheets ◽  
Global Flow ◽  
Low Mass ◽  
The Difference ◽  
Water Simulation ◽  
Turbulent Water

When we perform particle-based water simulation, water particles are often increased dramatically because of particle splitting around breaking holes to maintain the thin fluid sheets. Because most of the existing approaches do not consider the volume of the water particles, the water particles must have a very low mass to satisfy the law of the conservation of mass. This phenomenon smears the motion of the water, which would otherwise result in splashing, thereby resulting in artifacts such as numerical dissipation. Thus, we propose a new fluid-implicit, particle-based framework for maintaining and representing the thin sheets and turbulent flows of water. After splitting the water particles, the proposed method uses the ghost density and ghost mass to redistribute the difference in mass based on the volume of the water particles. Next, small-scale turbulent flows are formed in local regions and transferred in a smooth manner to the global flow field. Our results show us the turbulence details as well as the thin sheets of water, thereby obtaining an aesthetically pleasing improvement compared with existing methods.

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