Chaos in Electric Drive Systems
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Chaotic Advection, Diffusion, and Reactions in Open Flows Tamás Tél, György Károlyi, Áron Péntek, István Scheuring, Zoltán Toroczkai, Celso Grebogi, and James Kadtke
Chaotic advection, diffusion, and reactions in open flows Tamás Tél, György Károlyi, Áron Péntek, István Scheuring, Zoltán Toroczkai, Celso Grebogi, and James Kadtke Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science 10, 89 (2000); doi: 10.1063/1.166478 View online: http://dx.doi.org/10.1063/1.166478 View Table of Contents: http://scitation.aip.org/content/aip/journal/chaos/10/1?ver=pdfcov Published by the AIP Publishing This article is copyrighted as indicated in the article. Reuse of AIP content is subject to the terms at: http://scitation.aip.org/termsconditions. Downloaded to IP: 128.173.125.76 On: Mon, 24 Mar 2014 14:44:01 CHAOS VOLUME 10, NUMBER 1 MARCH 2000 Chaotic advection, diffusion, and reactions in open flows Tama´sTe´l Institute for Theoretical Physics, Eo¨tvo¨s University, P.O. Box 32, H-1518 Budapest, Hungary Gyo¨rgy Ka´rolyi Department of Civil Engineering Mechanics, Technical University of Budapest, Mu¨egyetem rpk. 3, H-1521 Budapest, Hungary A´ ron Pe´ntek Marine Physical Laboratory, Scripps Institution of Oceanography, University of California at San Diego, La Jolla, California 92093-0238 Istva´n Scheuring Department of Plant Taxonomy and Ecology, Research Group of Ecology and Theoretical Biology, Eo¨tvo¨s University, Ludovika te´r 2, H-1083 Budapest, Hungary Zolta´n Toroczkai Department of Physics, University of Maryland, College Park, Maryland 20742-4111 and Department of Physics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0435 Celso Grebogi Institute for Plasma Research, University of Maryland, College Park, Maryland 20742 James Kadtke Marine Physical Laboratory, Scripps Institution of Oceanography, University of California at San Diego, La Jolla, California 92093-0238 ͑Received 30 July 1999; accepted for publication 8 November 1999͒ We review and generalize recent results on advection of particles in open time-periodic hydrodynamical flows. -
Nonlinear Dynamics of Duffing System with Fractional Order Damping
Proceedings of the ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2009 August 30 - September 2, 2009, San Diego, California, USA Proceedings of the ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2009 August 30 - September 2, 2009, San Diego, California, USA DETC2009-86401 DETC2009-86401 NONLINEAR DYNAMICS OF DUFFING SYSTEM WITH FRACTIONAL ORDER DAMPING Junyi Cao Chengbin Ma Research Institute of Diagnostics and Cybernetics, UM-SJTU Joint Institute, Shanghai Jiaotong School of Mechanical engineering, Xi’an Jiaotong University, Shanghai 200240, China University, Xi’an 710049, China [email protected] [email protected] Hang Xie Zhuangde Jiang Research Institute of Diagnostics and Cybernetics, Institute of Precision Engineering, School of School of Mechanical engineering, Xi’an Jiaotong Mechanical engineering, Xi'an Jiaotong University, University, Xi’an 710049, China Xi'an 710049, China ABSTRACT Although some of the mathematical issues remain unsolved, In this paper, nonlinear dynamics of Duffing system with fractional calculus based modeling of complicated dynamics is fractional order damping is investigated. The four order Runge- becoming a recent focus of interest. The dynamics of fractional Kutta method and ten order CFE-Euler methods are introduced order system equations for Chua, Lorenz, Rossler, Chen, Jerk to simulate the fractional order Duffing equations. The effect of and Duffing are mainly investigated [3-9]. It is obvious that the taking fractional order on the system dynamics is investigated chaotic attractors existing in their fractional systems have using phase diagrams, bifurcation diagrams and Poincare map. different fractional orders. -
A New Complex Duffing Oscillator Used in Complex Signal Detection
Article Electronics June 2012 Vol.57 No.17: 21852191 doi: 10.1007/s11434-012-5145-8 SPECIAL TOPICS: A new complex Duffing oscillator used in complex signal detection DENG XiaoYing*, LIU HaiBo & LONG Teng School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China Received December 12, 2011; accepted February 2, 2012 In view of the fact that complex signals are often used in the digital processing of certain systems such as digital communication and radar systems, a new complex Duffing equation is proposed. In addition, the dynamical behaviors are analyzed. By calculat- ing the maximal Lyapunov exponent and power spectrum, we prove that the proposed complex differential equation has a chaotic solution or a large-scale periodic one depending on different parameters. Based on the proposed equation, we present a complex chaotic oscillator detection system of the Duffing type. Such a dynamic system is sensitive to the initial conditions and highly immune to complex white Gaussian noise, so it can be used to detect a weak complex signal against a background of strong noise. Results of the Monte-Carlo simulation show that the proposed detection system can effectively detect complex single frequency signals and linear frequency modulation signals with a guaranteed low false alarm rate. complex Duffing equation, chaos detection, weak complex signal, signal-to-noise ratio, probability of detection Citation: Deng X Y, Liu H B, Long T. A new complex Duffing oscillator used in complex signal detection. Chin Sci Bull, 2012, 57: 21852191, doi: 10.1007/s11434-012-5145-8 Chaos theory is one of the most significant achievements of plex chaos control and synchronization, and so on. -
Complexity, Chaos, and the Duffing-Oscillator Model
Complexity, Chaos, and the Duffing-Oscillator Model: An Analysis of Inventory Fluctuations in Markets Varsha S. Kulkarni School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA Abstract: Apparently random financial fluctuations often exhibit varying levels of complex- ity, chaos. Given limited data, predictability of such time series becomes hard to infer. While efficient methods of Lyapunov exponent computation are devised, knowledge about the process driving the dynamics greatly facilitates the complex- ity analysis. This paper shows that quarterly inventory changes of wheat in the global market, during 1974-2012, follow a nonlinear deterministic process. Lya- punov exponents of these fluctuations are computed using sliding time windows each of length 131 quarters. Weakly chaotic behavior alternates with non-chaotic behavior over the entire period of analysis. More importantly, in this paper, a cubic dependence of price changes on inventory changes leads to establishment of deterministic Duffing-Oscillator-Model(DOM) as a suitable candidate for examin- ing inventory fluctuations of wheat. DOM represents the interaction of commodity production cycle with an external intervention in the market. Parameters obtained for shifting time zones by fitting the Fourier estimated time signals to DOM are able to generate responses that reproduce the true chaotic nature exhibited by the empirical signal at that time. Endowing the parameters with suitable mean- ings, one may infer that temporary changes in speculation reflect the pattern of arXiv:1308.1616v1 [q-fin.GN] 24 Jul 2013 inventory volatility that drives the transitions between chaotic and non-chaotic behavior. 1 Introduction Time series of financial fluctuations often tends to display complexity and chaos at varying levels. -
Instructional Experiments on Nonlinear Dynamics & Chaos (And
Bibliography of instructional experiments on nonlinear dynamics and chaos Page 1 of 20 Colorado Virtual Campus of Physics Mechanics & Nonlinear Dynamics Cluster Nonlinear Dynamics & Chaos Lab Instructional Experiments on Nonlinear Dynamics & Chaos (and some related theory papers) overviews of nonlinear & chaotic dynamics prototypical nonlinear equations and their simulation analysis of data from chaotic systems control of chaos fractals solitons chaos in Hamiltonian/nondissipative systems & Lagrangian chaos in fluid flow quantum chaos nonlinear oscillators, vibrations & strings chaotic electronic circuits coupled systems, mode interaction & synchronization bouncing ball, dripping faucet, kicked rotor & other discrete interval dynamics nonlinear dynamics of the pendulum inverted pendulum swinging Atwood's machine pumping a swing parametric instability instabilities, bifurcations & catastrophes chemical and biological oscillators & reaction/diffusions systems other pattern forming systems & self-organized criticality miscellaneous nonlinear & chaotic systems -overviews of nonlinear & chaotic dynamics To top? Briggs, K. (1987), "Simple experiments in chaotic dynamics," Am. J. Phys. 55 (12), 1083-9. Hilborn, R. C. (2004), "Sea gulls, butterflies, and grasshoppers: a brief history of the butterfly effect in nonlinear dynamics," Am. J. Phys. 72 (4), 425-7. Hilborn, R. C. and N. B. Tufillaro (1997), "Resource Letter: ND-1: nonlinear dynamics," Am. J. Phys. 65 (9), 822-34. Laws, P. W. (2004), "A unit on oscillations, determinism and chaos for introductory physics students," Am. J. Phys. 72 (4), 446-52. Sungar, N., J. P. Sharpe, M. J. Moelter, N. Fleishon, K. Morrison, J. McDill, and R. Schoonover (2001), "A laboratory-based nonlinear dynamics course for science and engineering students," Am. J. Phys. 69 (5), 591-7. http://carbon.cudenver.edu/~rtagg/CVCP/Ctr_dynamics/Lab_nonlinear_dyn/Bibex_nonline.. -
Chapter 8 Nonlinear Systems
Chapter 8 Nonlinear systems 8.1 Linearization, critical points, and equilibria Note: 1 lecture, §6.1–§6.2 in [EP], §9.2–§9.3 in [BD] Except for a few brief detours in chapter 1, we considered mostly linear equations. Linear equations suffice in many applications, but in reality most phenomena require nonlinear equations. Nonlinear equations, however, are notoriously more difficult to understand than linear ones, and many strange new phenomena appear when we allow our equations to be nonlinear. Not to worry, we did not waste all this time studying linear equations. Nonlinear equations can often be approximated by linear ones if we only need a solution “locally,” for example, only for a short period of time, or only for certain parameters. Understanding linear equations can also give us qualitative understanding about a more general nonlinear problem. The idea is similar to what you did in calculus in trying to approximate a function by a line with the right slope. In § 2.4 we looked at the pendulum of length L. The goal was to solve for the angle θ(t) as a function of the time t. The equation for the setup is the nonlinear equation L g θ�� + sinθ=0. θ L Instead of solving this equation, we solved the rather easier linear equation g θ�� + θ=0. L While the solution to the linear equation is not exactly what we were looking for, it is rather close to the original, as long as the angleθ is small and the time period involved is short. You might ask: Why don’t we just solve the nonlinear problem? Well, it might be very difficult, impractical, or impossible to solve analytically, depending on the equation in question. -
Table of Contents (Print, Part 2)
PERIODICALS PHYSICAL REVIEW E Postmaster send address changes to: For editorial and subscription correspondence, APS Subscription Services please see inside front cover Suite 1NO1 „ISSN: 1539-3755… 2 Huntington Quadrangle Melville, NY 11747-4502 THIRD SERIES, VOLUME 75, NUMBER 6 CONTENTS JUNE 2007 PART 2: STATISTICAL AND NONLINEAR PHYSICS, FLUID DYNAMICS, AND RELATED TOPICS RAPID COMMUNICATIONS Chaos and pattern formation Giant acceleration in slow-fast space-periodic Hamiltonian systems (4 pages) ........................... 065201͑R͒ D. V. Makarov and M. Yu. Uleysky Controlling spatiotemporal chaos using multiple delays (4 pages) ..................................... 065202͑R͒ Alexander Ahlborn and Ulrich Parlitz Signatures of fractal clustering of aerosols advected under gravity (4 pages) ............................ 065203͑R͒ Rafael D. Vilela, Tamás Tél, Alessandro P. S. de Moura, and Celso Grebogi Fluid dynamics Clustering of matter in waves and currents (4 pages) ............................................... 065301͑R͒ Marija Vucelja, Gregory Falkovich, and Itzhak Fouxon Experimental study of the bursting of inviscid bubbles (4 pages) ..................................... 065302͑R͒ Frank Müller, Ulrike Kornek, and Ralf Stannarius Surface thermal capacity and its effects on the boundary conditions at fluid-fluid interfaces (4 pages) ....... 065303͑R͒ Kausik S. Das and C. A. Ward Plasma physics Direct evidence of strongly inhomogeneous energy deposition in target heating with laser-produced ion beams (4 pages) .................................................................................. -
An Experimental Approach to Nonlinear Dynamics and Chaos Is a Textb O Ok and A
An Exp erimental Approach to Nonlinear Dynamics and Chaos by Nicholas B Tullaro Tyler Abb ott and Jeremiah PReilly INTERNET ADDRESSES nbtreededu c COPYRIGHT TUFILLARO ABBOTT AND REILLY Published by AddisonWesley v b Novemb er i ii iii iv Contents Preface xi Intro duction What is nonlinear dynamics What is in this b o ok Some Terminology Maps Flows and Fractals References and Notes Bouncing Ball Intro duction Mo del Stationary Table Impact Relation for the Oscillating Table The Equations of Motion Phase and Velo city Maps Parameters High Bounce Approximation Qualitative Description of Motions Trapping Region Equilibrium Solutions Sticking Solutions Perio d One Orbits and Perio d Doubling Chaotic Motions Attractors Bifurcation Diagrams References and Notes Problems Quadratic Map Intro duction Iteration and Dierentiation Graphical Metho d Fixed Points v vi CONTENTS Perio dic Orbits Graphical Metho d Perio d One Orbits Perio d Two Orbit Stability Diagram Bifurcation Diagram Lo cal Bifurcation Theory Saddleno de Perio d Doubling Transcritical Perio d Doubling Ad Innitum Sarkovskiis Theorem Sensitive Dep endence Fully Develop ed Chaos -
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems Wen-Xu Wanga;b, Ying-Cheng Laic;d;e, and Celso Grebogie a School of Systems Science, Beijing Normal University, Beijing, 100875, China b Business School, University of Shanghai for Science and Technology, Shanghai 200093, China c School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA d Department of Physics, Arizona State University, Tempe, Arizona 85287, USA e Institute for Complex Systems and Mathematical Biology, King’s College, University of Aberdeen, Aberdeen AB24 3UE, UK Wen-Xu Wang, Ying-Cheng Lai, and Celso Grebogi 1 Abstract The problem of reconstructing nonlinear and complex dynamical systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. The classic approach to phase-space re- construction through the methodology of delay-coordinate embedding has been practiced for more than three decades, but the paradigm is effective mostly for low-dimensional dynamical systems. Often, the methodology yields only a topological correspondence of the original system. There are situations in various fields of science and engineering where the systems of interest are complex and high dimensional with many interacting components. A complex system typically exhibits a rich variety of collective dynamics, and it is of great interest to be able to detect, classify, under- stand, predict, and control the dynamics using data that are becoming increasingly accessible due to the advances of modern information technology. To accomplish these tasks, especially prediction and control, an accurate reconstruction of the original system is required. -
Weak Signal Detection Research Based on Duffing Oscillator Used
JOURNAL OF COMPUTERS, VOL. 6, NO. 2, FEBRUARY 2011 359 Weak Signal Detection Research Based on Duffing Oscillator Used for Downhole Communication Xuanchao Liu School of Electronic Engineering, Xi’an Shiyou University, Xi’an, China [email protected] Xiaolong Liu School of Electronic Engineering, Xi’an Shiyou University, Xi’an, China [email protected] Abstract—Weak signal detection is very important in the information transmission. Thus, the study of weak signal downhole acoustic telemetry system. This paper introduces detection technology is of great significance. the Duffing oscillator weak signal detection method for the Weak signal detection is an emerging technical downhole acoustic telemetry systems. First, by solving the discipline, mainly researches detection principle and Duffing equation, analyzed the dynamics characteristic of method of the weak signal buried in the noise, which is a Duffing oscillator and weak signal detection principle; and then on this basis, built Duffing oscillator circuit based on kind of comprehensive technology in the signal the Duffing equation, by circuit simulation to study the processing technology, mainly utilizes the modern Duffing circuit sensitive to different initial parameters, electronics, the information theory and the physics conducted a detailed analysis for how the different method, to analyze reason and rule the noise producting, parameters impacted the system statuses and the low-pass to study statistical nature and difference of the detecting filter with simplicity and availability was proposed for signal and the noise; uses a series of signal processing signal demodulation. The results show that the method method, achieves the goal that weak signal buried in the could effectively detect the weak changes of input signal and background noise is detected successfully. -
Strange Attractors That Are Not Chaotic
Physica 13D (1984) 261-268 North-Holland, Amsterdam STRANGE ATTRACTORS THAT ARE NOT CHAOTIC Celso GREBOGI, a Edward OTT, ~b Steven PELIKAN, c and James A. YORKE d Received 7. February 1984 It is shown that in certain types of dynamical systems it is possible to have attractors which are strange but not chaotic. Here we use the word strange to refer to the geometry or shape of the attracting set, while the word chaotic refers to the dynamics of orbits on the attractor (in particular, the exponential divergence of nearby trajectories). We first give examples for which it can be demonstrated that there is a strange nonchaotic attractor. These examples apply to a class of maps which model nonlinear oscillators (continuous time) which are externally driven at two incommensurate frequencies. It is then shown that such attractors are persistent under perturbations which preserve the original system type (i.e., there are two incommensurate external driving frequencies). This suggests that, for systems of the type which we have considered, nonchaotic strange attractors may be expected to occur for a finite interval of parameter values. On the other hand, when small perturbations which do not preserve the system type are numerically introduced the strange nonchaotic attractor is observed to be converted to a periodic or chaotic orbit. Thus we conjecture that, in general, continuous time systems (" flows") which are not externally driven at two incommensurate frequencies should not be expected to have strange nonchaotic attractors except possibly on a set of measure zero in the parameter space. 1. Introduction and definitions ties [1] such as noninteger fractal dimension, Cantor set structure, or nowhere differentiability. -
3 the Van Der Pol Oscillator 19 3.1Themethodofaveraging
Lecture Notes on Nonlinear Vibrations Richard H. Rand Dept. Theoretical & Applied Mechanics Cornell University Ithaca NY 14853 [email protected] http://www.tam.cornell.edu/randdocs/ version 45 Copyright 2003 by Richard H. Rand 1 R.Rand Nonlinear Vibrations 2 Contents 1PhasePlane 4 1.1ClassificationofLinearSystems............................ 4 1.2 Lyapunov Stability ................................... 5 1.3 Structural Stability ................................... 7 1.4Examples........................................ 8 1.5Problems......................................... 8 1.6 Appendix: Lyapunov’s Direct Method ........................ 10 2 The Duffing Oscillator 13 2.1Lindstedt’sMethod................................... 14 2.2 Elliptic Functions . ................................... 15 2.3Problems......................................... 17 3 The van der Pol Oscillator 19 3.1TheMethodofAveraging............................... 19 3.2HopfBifurcations.................................... 23 3.3HomoclinicBifurcations................................ 24 3.4 Relaxation Oscillations ................................. 27 3.5 The van der Pol oscillator at Infinity . ........................ 29 3.6Example......................................... 32 3.7Problems......................................... 32 4 The Forced Duffing Oscillator 34 4.1TwoVariableExpansionMethod........................... 35 4.2CuspCatastrophe.................................... 38 4.3Problems......................................... 39 5 The Forced van der Pol Oscillator 40 5.1Entrainment......................................