Combustion noise is scale-free: transition from scale-free to order at the onset of thermoacoustic instability

2015 ◽  
Vol 772 ◽  
pp. 225-245 ◽  
Author(s):  
Meenatchidevi Murugesan ◽  
R. I. Sujith
Keyword(s):  
Complex Networks ◽  
Scale Invariance ◽  
Time Series Data ◽  
Pressure Fluctuations ◽  
Combustion Noise ◽  
Series Data ◽  
Turbulent Reacting Flows ◽  
Thermoacoustic Instability ◽  
Scale Free ◽  
Visibility Algorithm

We investigate the scale invariance of combustion noise generated from turbulent reacting flows in a confined environment using complex networks. The time series data of unsteady pressure, which is the indicative of spatiotemporal changes happening in the combustor, is converted into complex networks using the visibility algorithm. We show that the complex networks obtained from the low-amplitude, aperiodic pressure fluctuations during combustion noise have scale-free structure. The power-law distributions of connections in the scale-free network are related to the scale invariance of combustion noise. We also show that the scale-free feature of combustion noise disappears and order emerges in the complex network topology during the transition from combustion noise to combustion instability. The use of complex networks enables us to formalize the identification of the pattern (i.e. scale-free to order) during the transition from combustion noise to thermoacoustic instability as a structural change in topology of the network.

Protection and Identification of Thermoacoustic Azimuthal Modes

2021 ◽  
Vol 143 (4) ◽  
Author(s):  
G. Ghirardo ◽  
F. Gant ◽  
F. Boudy ◽  
M. R. Bothien
Keyword(s):  
Acoustic Field ◽  
Invariant Manifolds ◽  
Time Series Data ◽  
Pressure Sensors ◽  
Fourier Component ◽  
Combustion Noise ◽  
Series Data ◽  
Order Problem ◽  
Protection Scheme ◽  
Low Order

Abstract This paper first characterizes the acoustic field of two annular combustors by means of data from acoustic pressure sensors. In particular, the amplitude, orientation, and nature of the acoustic field of azimuthal order n are characterized. The dependence of the pulsation amplitude on the azimuthal location in the chamber is discussed, and a protection scheme making use of just one sensor is proposed. The governing equations are then introduced, and a low-order model of the instabilities is discussed. The model accounts for the nonlinear response of M distinct flames, for system acoustic losses by means of an acoustic damping coefficient α and for the turbulent combustion noise, modeled by means of the background noise coefficient σ. Keeping the response of the flames arbitrary and in principle different from flame to flame, we show that, together with α and σ, only the sum of their responses and their 2n Fourier component in the azimuthal direction affect the dynamics of the azimuthal instability. The existing result that only this 2n Fourier component affects the stability of standing limit-cycle solutions is recovered. It is found that this result applies also to the case of a nonhomogeneous flame response in the annulus, and to flame responses that respond to the azimuthal acoustic velocity. Finally, a parametric flame model is proposed, depending on a linear driving gain β and a nonlinear saturation constant κ. The model is first mapped from continuous time to discrete time, and then recast as a probabilistic Markovian model. The identification of the parameters {α,β,κ,σ} is then carried out on engine time-series data. The optimal four parameters {α,σ,β,κ} are estimated as the values that maximize the data likelihood. Once the parameters have been estimated, the phase space of the identified low-order problem is discussed on selected invariant manifolds of the dynamical system.

scholarly journals Spatiotemporal dynamics during the transition to thermoacoustic instability: Effect of varying turbulence intensities

2018 ◽  
Vol 10 (4) ◽  
pp. 337-350 ◽  
Author(s):  
Nitin B George ◽  
Vishnu R Unni ◽  
Manikandan Raghunathan ◽  
RI Sujith
Keyword(s):  
High Speed ◽  
Equivalence Ratio ◽  
Pressure Fluctuations ◽  
Spatial Dynamics ◽  
Local Heat ◽  
Spatiotemporal Dynamics ◽  
Combustion Noise ◽  
Pressure Oscillations ◽  
Thermoacoustic Instability ◽  
Turbulence Generator

An experimental study on a turbulent, swirl-stabilized backward facing step combustor is conducted to understand the spatiotemporal dynamics during the transition from combustion noise to thermoacoustic instability. By using a turbulence generator, we investigate the change in the spatiotemporal dynamics during this transition for added turbulence intensities. High-speed CH* images of the flame (representative of the field of local heat release rate fluctuations ([Formula: see text]( x, y, t))) and simultaneous unsteady pressure fluctuations ([Formula: see text]( t)) are acquired for different equivalence ratios. In the study, without the turbulence generator, as the equivalence ratio is reduced from near stoichiometric values, we observe an emergence of coherence in the spatial dynamics during the occurrence of intermittency, enroute to thermoacoustic instability. As the turbulence intensity is increased using the turbulence generator, we find that there is an advanced onset of thermoacoustic instability. Spatial statistics and the instantaneous fields of [Formula: see text] show that during the transition from combustion noise to thermoacoustic instability, the emergence of coherent spatial structures in the instantaneous fields of [Formula: see text] for the experiments with higher turbulence intensities is advanced. However, as the equivalence ratio is reduced further, we notice that higher turbulence intensities result in the reduction of the strength of the pressure oscillations during the state of thermoacoustic instability. We find that, at these low equivalence ratios, there is a decrease in the coherence due to the dispersal of [Formula: see text], which explains the reduction in the strength of the pressure oscillations.

Analysis of the attention to COVID-19 epidemic based on visibility graph network

2021 ◽  
pp. 2150316
Author(s):  
Qingxiang Feng ◽  
Haipeng Wei ◽  
Jun Hu ◽  
Wenzhe Xu ◽  
Fan Li ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Time Series Data ◽  
Search Behavior ◽  
Series Data ◽  
Global Public Health ◽  
Health Crisis ◽  
Time Space ◽  
Epidemic Period ◽  
Visibility Algorithm

Most of the existing researches on public health events focus on the number and duration of events in a year or month, which are carried out by regression equation. COVID-19 epidemic, which was discovered in Wuhan, Hubei Province, quickly spread to the whole country, and then appeared as a global public health event. During the epidemic period, Chinese netizens inquired about the dynamics of COVID-19 epidemic through Baidu search platform, and learned about relevant epidemic prevention information. These groups’ search behavior data not only reflect people’s attention to COVID-19 epidemic, but also contain the stage characteristics and evolution trend of COVID-19 epidemic. Therefore, the time, space and attribute laws of propagation of COVID-19 epidemic can be discovered by deeply mining more information in the time series data of search behavior. In this study, it is found that transforming time series data into visibility network through the principle of visibility algorithm can dig more hidden information in time series data, which may help us fully understand the attention to COVID-19 epidemic in Chinese provinces and cities, and evaluate the deficiencies of early warning and prevention of major epidemics. What’s more, it will improve the ability to cope with public health crisis and social decision-making level.

scholarly journals Analyzing Levels of Concern About Joint Punishment for Dishonesty Using the Visibility Graph Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiqiang Qu ◽  
Yujie Zhang ◽  
Fan Li
Keyword(s):  
Time Series ◽  
Topological Structure ◽  
Time Series Data ◽  
Graph Algorithm ◽  
Visibility Graph ◽  
Series Data ◽  
Scale Free ◽  
Central Node ◽  
Important Means ◽  
Search Index

Joint punishment for dishonesty is an important means of administrative regulation. This research analyzed the dynamic characteristics of time series data from the Baidu search index using the keywords “joint punishment for dishonesty” based on a visibility graph network. Applying a visibility graph algorithm, time series data from the Baidu Index was transformed into complex networks, with parameters calculated to analyze the topological structure. Results showed differences in the use of joint punishment for dishonesty in certain provinces by calculating the parameters of the time series network from January 1, 2020 to May 27, 2021; it was also shown that most of the networks were scale-free. Finally, the results of K-means clustering showed that the 31 provinces (excluding Hong Kong, Macao and Taiwan) can be divided into four types. Meanwhile, by analyzing the national Baidu Index data from 2020 to May 2021, the period of the time series data and the influence range of the central node were found.

Multifractality in combustion noise: predicting an impending combustion instability

2014 ◽  
Vol 747 ◽  
pp. 635-655 ◽  
Author(s):  
Vineeth Nair ◽  
R. I. Sujith
Keyword(s):  
Scale Invariance ◽  
Prognostic Value ◽  
Combustion Instability ◽  
Pressure Fluctuations ◽  
High Amplitude ◽  
Combustion Noise ◽  
Warning Signals ◽  
Stochastic Background ◽  
Turbulent Flow Field ◽  
Low Amplitude

AbstractThe transition in dynamics from low-amplitude, aperiodic, combustion noise to high-amplitude, periodic, combustion instability in confined, combustion environments was studied experimentally in a laboratory-scale combustor with two different flameholding devices in a turbulent flow field. We show that the low-amplitude, irregular pressure fluctuations acquired during stable regimes, termed ‘combustion noise’, display scale invariance and have a multifractal signature that disappears at the onset of combustion instability. Traditional analysis often treats combustion noise and combustion instability as acoustic problems wherein the irregular fluctuations observed in experiments are often considered as a stochastic background to the dynamics. We demonstrate that the irregular fluctuations contain useful information of prognostic value by defining representative measures such as Hurst exponents that can act as early warning signals to impending instability in fielded combustors.

INFERENCE OF SCALE-FREE NETWORKS FROM GENE EXPRESSION TIME SERIES

2006 ◽  
Vol 04 (02) ◽  
pp. 503-514 ◽  
Author(s):  
TOMINAGA DAISUKE ◽  
PAUL HORTON
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Biological Networks ◽  
Gene Networks ◽  
Time Series Data ◽  
Model Simulation ◽  
False Negative ◽  
Series Data ◽  
Scale Free ◽  
Scale Free Networks

Quantitative time-series observation of gene expression is becoming possible, for example by cell array technology. However, there are no practical methods with which to infer network structures using only observed time-series data. As most computational models of biological networks for continuous time-series data have a high degree of freedom, it is almost impossible to infer the correct structures. On the other hand, it has been reported that some kinds of biological networks, such as gene networks and metabolic pathways, may have scale-free properties. We hypothesize that the architecture of inferred biological network models can be restricted to scale-free networks. We developed an inference algorithm for biological networks using only time-series data by introducing such a restriction. We adopt the S-system as the network model, and a distributed genetic algorithm to optimize models to fit its simulated results to observed time series data. We have tested our algorithm on a case study (simulated data). We compared optimization under no restriction, which allows for a fully connected network, and under the restriction that the total number of links must equal that expected from a scale free network. The restriction reduced both false positive and false negative estimation of the links and also the differences between model simulation and the given time-series data.

scholarly journals Weighted recurrence networks for the analysis of time-series data

2019 ◽  
Vol 475 (2221) ◽  
pp. 20180256 ◽  
Author(s):  
Rinku Jacob ◽  
K. P. Harikrishnan ◽  
R. Misra ◽  
G. Ambika
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Chaotic Attractor ◽  
Time Series Data ◽  
Strength Distribution ◽  
Clustering Coefficient ◽  
Series Data ◽  
Characteristic Path Length ◽  
New Class ◽  
Analysis Of Time Series

Recurrence networks (RNs) have become very popular tools for the nonlinear analysis of time-series data. They are unweighted and undirected complex networks constructed with specific criteria from time series. In this work, we propose a method to construct a ‘weighted recurrence network’ from a time series and show that it can reveal useful information regarding the structure of a chaotic attractor which the usual unweighted RN cannot provide. Especially, a network measure, the node strength distribution, from every chaotic attractor follows a power law (with exponential cut off at the tail) with an index characteristic to the fractal structure of the attractor. This provides a new class among complex networks to which networks from all standard chaotic attractors are found to belong. Two other prominent network measures, clustering coefficient and characteristic path length, are generalized and their utility in discriminating chaotic dynamics from noise is highlighted. As an application of the proposed measure, we present an analysis of variable star light curves whose behaviour has been reported to be strange non-chaotic in a recent study. Our numerical results indicate that the weighted recurrence network and the associated measures can become potentially important tools for the analysis of short and noisy time series from the real world.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data
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