Deterministic Chaos in Laminar Premixed Flames: Experimental Classification of Chaotic Dynamics

1993 ◽  
Vol 94 (1-6) ◽  
pp. 87-101 ◽  
Author(s):  
MOHAMED el-HAMDI ◽  
MICHAEL GORMAN ◽  
KAY A. ROBBINS
2004 ◽  
Vol 14 (10) ◽  
pp. 3671-3678
Author(s):  
G. P. BYSTRAI ◽  
S. I. IVANOVA ◽  
S. I. STUDENOK

A second-order nonlinear differential equation with an aftereffect for the density of a thin homogeneous layer on a liquid and vapor interface is considered. The acts of evaporation and condensation of molecules, which are regarded as periodic "impacts", excite the layer. The mentioned NDE is integrated over a finite time interval to find a 2D (two-dimensional) mapping whose numerical solution describes the chaotic dynamics of density and pressure in time. The algorithms of constructing bifurcation diagrams, Lyapunov's exponents and Kolmogorov's entropy for systems with first-order, second-order phase transitions and Van der Waals' systems were elaborated. This approach allows to associate such concepts as phase transition, deterministic chaos and nonlinear processes. It also allows to answer a question whether deterministic chaos occurs in systems with phase transitions and how fast the information about starting conditions is lost within them.


2015 ◽  
Vol 4 (1) ◽  
pp. 31-39
Author(s):  
Берестин ◽  
D. Berestin ◽  
Игуменов ◽  
D. Igumenov ◽  
Рассадина ◽  
...  

The results of the stochastic analysis of postural tremor (as alleged involuntary movement) and tapping (as supposedly voluntary movement) are considered in a comparative perspective. It is proved that the stochastic analysis of the results of chaotic dynamics in tremorogramms and tapping does not give significant differences (absence of voluntariness). Typically, all samples are significantly different and it is impossible to distinguish subjects in their tremorogramms or tapingramm. The significant differences between sites of tremorogramms in terms of a normal distribution or a non-parametric distribution are demonstrated. A continuous variation of the distribution function is observed: parametric distribution shifts to non-parametric distribution, but among themselves they (distribution function) are all different. It is well known that the unpredicta-bility and continuing changes in the state are characteristic feature of chaos. The evidence of special kind of chaos in biosystems which differs significantly from the deterministic chaos of Tom – Ar-nold is given.


1987 ◽  
Vol 42 (2) ◽  
pp. 136-142 ◽  
Author(s):  
H. Herzel ◽  
W. Ebeling ◽  
Th. Schulmeister

Biochemical models capable of sustained oscillations and deterministic chaos are investigated. Chaos is characterized by exponential separation of near-by trajectories in the long-term average. However, we observed rather large deviations from purely exponential separation termed "nonuniformity". A quantitative description and consequences of nonuniformity are discussed.Furthermore, the influence of short-correlated noise is treated using next-amplitude maps and Lyapunov exponents. Drastic amplification of fluctuations in non-chaotic systems and relative robustness of chaos were found.


1998 ◽  
Vol 08 (06) ◽  
pp. 1325-1333 ◽  
Author(s):  
Ranjit Kumar Upadhyay ◽  
S. R. K. Iyengar ◽  
Vikas Rai

Deterministic chaos has been studied extensively in various fields. Some of the ideas emerging out of these studies have been put to novel applications. However, it is unknown whether natural ecological systems support chaotic dynamics. There is no concrete evidence which suggests that ecosystem evolution is chaotic in certain situations. This is very intriguing because ecosystems do possess all the necessary qualifications to be able to support such a dynamical behavior. The present paper attempts to answer the above question with the help of a few systems modeling different but very common ecological situations. A new methodology for the analysis of a class of model ecological systems is presented. Simulation experiments suggest that natural terrestrial systems are not suitable candidates where one should look for chaos. Additionally, our study also points out that the failure of attempts to observe chaos in natural populations might have resulted because biological interactions are not conducive for such a behavior to be supported. The cause of these failures may not be the poor data quality or demerits in the analysis techniques.


2006 ◽  
Vol 3 (2) ◽  
pp. 365-394 ◽  
Author(s):  
J. D. Phillips

Abstract. Geomorphic systems are typically nonlinear, owing largely to their threshold-dominated nature (but due to other factors as well). Nonlinear geomorphic systems may exhibit complex behaviors not possible in linear systems, including dynamical instability and deterministic chaos. The latter are common in geomorphology, indicating that small, short-lived changes may produce disproportionately large and long-lived results; that evidence of geomorphic change may not reflect proportionally large external forcings; and that geomorphic systems may have multiple potential response trajectories or modes of adjustment to change. Instability and chaos do not preclude predictability, but do modify the context of predictability. The presence of chaotic dynamics inhibits or excludes some forms of predicability and prediction techniques, but does not preclude, and enables, others. These dynamics also make spatial and historical contingency inevitable: geography and history matter. Geomorphic systems are thus governed by a combination of ''global'' laws, generalizations and relationships that are largely (if not wholly) independent of time and place, and ''local'' place and/or time-contingent factors. The more factors incorporated in the representation of any geomorphic system, the more singular the results or description are. Generalization is enhanced by reducing rather than increasing the number of factors considered. Prediction of geomorphic responses calls for a recursive approach whereby global laws and local contingencies are used to constrain each other. More specifically a methodology whereby local details are embedded within simple but more highly general phenomenological models is advocated. As landscapes and landforms change in response to climate and other forcings, it cannot be assumed that geomorphic systems progress along any particular pathway. Geomorphic systems are evolutionary in the sense of being path dependent, and historically and geographically contingent. Assessing and predicting geomorphic responses obliges us to engage these contingencies, which often arise from nonlinear complexities. We are obliged, then, to practice evolutionary geomorphology: an approach to the study of surface processes and landforms with recognizes multiple possible historical pathways rathen than an inexorable progression toward some equilbribrium state or along a cyclic pattern.


Author(s):  
Vijaykumar Sathyamurthi ◽  
Debjyoti Banerjee

Saturated pool boiling experiments are conducted over silicon substrates with and without Multi-walled Carbon Nanotubes (MWCNT) with PF-5060 as the test fluid. Micro-fabricated thin film thermocouples located on the substrate acquire surface temperature fluctuation data at 1 kHz frequency. The high frequency surface temperature data is analyzed for the presence of chaotic dynamics. The shareware code, TISEAN© is used in analysis of the temperature time-series. Results show the presence of low-dimensional deterministic chaos, near Critical Heat Flux (CHF) and in some parts of the Fully Developed Nucleate Boiling (FDNB) regime. Some evidence of chaotic dynamics is also obtained for the film boiling regimes. Singular value decomposition is employed to generate pseudo-phase plots of the attractor. In contrast to previous studies involving multiple nucleation sites, the pseudo-phase plots show the presence of multi-fractal structure at high heat fluxes and in the film boiling regime. An estimate of invariant quantities such as correlation dimensions and Lyapunov exponents reveals the change in attractor geometry with heat flux levels. No significant impact of surface texturing is visible in terms of the invariant quantities.


2001 ◽  
Vol 11 (06) ◽  
pp. 1607-1629 ◽  
Author(s):  
ROBERT KOZMA ◽  
WALTER J. FREEMAN

A fundamental tenet of the theory of deterministic chaos holds that infinitesimal variation in the initial conditions of a network that is operating in the basin of a low-dimensional chaotic attractor causes the various trajectories to diverge from each other quickly. This "sensitivity to initial conditions" might seem to hold promise for signal detection, owing to an implied capacity for distinguishing small differences in patterns. However, this sensitivity is incompatible with pattern classification, because it amplifies irrelevant differences in incomplete patterns belonging to the same class, and it renders the network easily corrupted by noise. Here a theory of stochastic chaos is developed, in which aperiodic outputs with 1/f2 spectra are formed by the interaction of globally connected nodes that are individually governed by point attractors under perturbation by continuous white noise. The interaction leads to a high-dimensional global chaotic attractor that governs the entire array of nodes. An example is our spatially distributed KIII network that is derived from studies of the olfactory system, and that is stabilized by additive noise modeled on biological noise sources. Systematic parameterization of the interaction strengths corresponding to synaptic gains among nodes representing excitatory and inhibitory neuron populations enables the formation of a robust high-dimensional global chaotic attractor. Reinforcement learning from examples of patterns to be classified using habituation and association creates lower dimensional local basins, which form a global attractor landscape with one basin for each class. Thereafter, presentation of incomplete examples of a test pattern leads to confinement of the KIII network in the basin corresponding to that pattern, which constitutes many-to-one generalization. The capture after learning is expressed by a stereotypical spatial pattern of amplitude modulation of a chaotic carrier wave. Sensitivity to initial conditions is no longer an issue. Scaling of the additive noise as a parameter optimizes the classification of data sets in a manner that is comparable to stochastic resonance. The local basins constitute dynamical memories that solve difficult problems in classifying data sets that are not linearly separable. New local basins can be added quickly from very few examples without loss of existing basins. The attractor landscape enables the KIII set to provide an interface between noisy, unconstrained environments and conventional pattern classifiers. Examples given here of its robust performance include fault detection in small machine parts and the classification of spatiotemporal EEG patterns from rabbits trained to discriminate visual stimuli.


2009 ◽  
Vol 19 (07) ◽  
pp. 2363-2375 ◽  
Author(s):  
MARCO A. MONTAVA BELDA

Certain systems present chaotic dynamics when subjected to a regular periodic input. In a study of a nonlinear model of an electromechanical transducer, its dynamic stability is analyzed and it is observed to present chaotic dynamics when a squared signal is introduced as input to the excitor circuit voltage. It is demonstrated that the chaotic movement is due to the periodic modification in the attraction basin of the state space, caused by the input varying in time. Varying the input causes the system to cross saddle type bifurcation values in which points of equilibrium appear and disappear, periodically modifying the qualitative aspects of the system's phase space. This paper describes the deterministic chaos generation by the regular and periodic modification of the properties of the phase space.


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