Hamiltonian Chaos in a Model Alveolus

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
F. S. Henry ◽  
F. E. Laine-Pearson ◽  
A. Tsuda
Keyword(s):  
Reynolds Number ◽  
Fixed Point ◽  
Lyapunov Exponent ◽  
Fine Particles ◽  
Hamiltonian Dynamics ◽  
Maximal Lyapunov Exponent ◽  
Poincaré Sections ◽  
Pulmonary Acinus ◽  
Hamiltonian Chaos ◽  
Poincare Sections

In the pulmonary acinus, the airflow Reynolds number is usually much less than unity and hence the flow might be expected to be reversible. However, this does not appear to be the case as a significant portion of the fine particles that reach the acinus remains there after exhalation. We believe that this irreversibility is at large a result of chaotic mixing in the alveoli of the acinar airways. To test this hypothesis, we solved numerically the equations for incompressible, pulsatile, flow in a rigid alveolated duct and tracked numerous fluid particles over many breathing cycles. The resulting Poincaré sections exhibit chains of islands on which particles travel. In the region between these chains of islands, some particles move chaotically. The presence of chaos is supported by the results of an estimate of the maximal Lyapunov exponent. It is shown that the streamfunction equation for this flow may be written in the form of a Hamiltonian system and that an expansion of this equation captures all the essential features of the Poincaré sections. Elements of Kolmogorov–Arnol’d–Moser theory, the Poincaré–Birkhoff fixed-point theorem, and associated Hamiltonian dynamics theory are then employed to confirm the existence of chaos in the flow in a rigid alveolated duct.

scholarly journals VALIDATING IDENTIFIED NONLINEAR MODELS WITH CHAOTIC DYNAMICS

1994 ◽  
Vol 04 (01) ◽  
pp. 109-125 ◽  
Author(s):  
LUIS A. AGUIRRE ◽  
S.A. BILLINGS
Keyword(s):  
Lyapunov Exponent ◽  
Correlation Dimension ◽  
Chaotic Dynamics ◽  
Nonlinear Models ◽  
Original System ◽  
Largest Lyapunov Exponent ◽  
Bifurcation Diagrams ◽  
Poincaré Sections ◽  
The Largest Lyapunov Exponent ◽  
Poincare Sections

This paper investigates the effectiveness of several criteria for validating models which exhibit chaotic dynamics. Embedded trajectories, Poincaré sections, bifurcation diagrams, the largest Lyapunov exponent and correlation dimension are considered. The Duffing-Ueda equation and four identified models are used as examples. The results show that models with similar invariants such as Poincaré sections, the largest Lyapunov exponent and correlation dimension may have very different bifurcation behaviours. This suggests that the requirement that an identified model should reproduce the bifurcation pattern of the original system is a very exacting criterion which is well suited for validation purposes.

Dynamic stability of running: The effects of speed and leg amputations on the maximal Lyapunov exponent

2013 ◽  
Vol 23 (4) ◽  
pp. 043131 ◽  
Author(s):  
Nicole Look ◽  
Christopher J. Arellano ◽  
Alena M. Grabowski ◽  
William J. McDermott ◽  
Rodger Kram ◽  
...  
Keyword(s):  
Lyapunov Exponent ◽  
Dynamic Stability ◽  
Maximal Lyapunov Exponent

Constructing Keyed Strong S-Box Using an Enhanced Quadratic Map

2021 ◽  
Vol 31 (10) ◽  
pp. 2150146
Author(s):  
Yuanyuan Si ◽  
Hongjun Liu ◽  
Yuehui Chen
Keyword(s):  
Phase Diagram ◽  
Fixed Point ◽  
Lyapunov Exponent ◽  
Uniform Distribution ◽  
Bifurcation Diagram ◽  
Experimental Results ◽  
Kolmogorov Entropy ◽  
Symmetric Cryptography ◽  
Quadratic Map ◽  
Nonlinear Component

As the only nonlinear component for symmetric cryptography, S-Box plays an important role. An S-Box may be vulnerable because of the existence of fixed point, reverse fixed point or short iteration cycles. To construct a keyed strong S-Box, first, a 2D enhanced quadratic map (EQM) was constructed, and its dynamic behaviors were analyzed through phase diagram, Lyapunov exponent, Kolmogorov entropy, bifurcation diagram and randomness testing. The results demonstrated that the state points of EQM have uniform distribution, ergodicity and better randomness. Then a keyed strong S-Box construction algorithm was designed based on EQM, and the fixed point, reverse fixed point, and short cycles were eliminated. Experimental results verified the algorithm’s feasibility and effectiveness.

A Chaotification model based on sine and cosecant functions for enhancing chaos

2021 ◽  
Author(s):  
Yuqing Li ◽  
Xing He ◽  
Dawen Xia
Keyword(s):  
Fixed Point ◽  
Lyapunov Exponent ◽  
Chaotic Maps ◽  
Dynamic Properties ◽  
Chaotic Map ◽  
Sample Entropy ◽  
One Dimensional ◽  
Model Based ◽  
Chaotic Region ◽  
Definition Of

Chaotic maps with higher chaotic complexity are urgently needed in many application scenarios. This paper proposes a chaotification model based on sine and cosecant functions (CMSC) to improve the dynamic properties of existing chaotic maps. CMSC can generate a new map with higher chaotic complexity by using the existing one-dimensional (1D) chaotic map as a seed map. To discuss the performance of CMSC, the chaos properties of CMSC are analyzed based on the mathematical definition of the Lyapunov exponent (LE). Then, three new maps are generated by applying three classical 1D chaotic maps to CMSC respectively, and the dynamic behaviors of the new maps are analyzed in terms of fixed point, bifurcation diagram, sample entropy (SE), etc. The results of the analysis demonstrate that the new maps have a larger chaotic region and excellent chaotic characteristics.

Deposition of Submicron Particles by Chaotic Mixing in the Pulmonary Acinus

2021 ◽  
pp. 145-161
Author(s):  
Akira Tsuda ◽  
Frank S. Henry
Keyword(s):  
Fluid Particle ◽  
Submicron Particles ◽  
Chaotic Mixing ◽  
Breathing Frequency ◽  
Particle Trajectories ◽  
Recirculating Flow ◽  
Pulmonary Acinus ◽  
Inhaled Particles ◽  
Hamiltonian Chaos ◽  
Qualitative Changes

In this review, the authors outline the evidence that emerged some 30 years ago that the mechanisms thought responsible for the deposition of submicron particles in the respiratory region of the lung were inadequate to explain the measured rate of deposition. They then discuss the background and theory of what is believed to be the missing mechanism, namely chaotic mixing. Specifically, they outline how that the recirculating flow in the alveoli has a range of frequencies of oscillation and some of these resonate with the breathing frequency. If the system is perturbed, the resonating frequencies break into chaos, and they discuss a number of practical ways in which the system can be disturbed. The perturbation of fluid particle trajectories results in Hamiltonian chaos, which produces qualitative changes in those trajectories. They end the review with a discussion of the effects of chaotic mixing on the deposition of inhaled particles in the respiratory region of the lung.

FINITE DIMENSIONAL STRUCTURE OF THE GPI DISCHARGE IN PATIENTS WITH PARKINSON'S DISEASE

2011 ◽  
Vol 21 (03) ◽  
pp. 175-186 ◽  
Author(s):  
DANIELA SABRINA ANDRES ◽  
DANIEL CERQUETTI ◽  
MARCELO MERELLO
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Lyapunov Exponent ◽  
Stochastic Systems ◽  
Rating Scale ◽  
Severe Disease ◽  
Dimensional Structure ◽  
Maximal Lyapunov Exponent ◽  
Embedding Dimension ◽  
Low Dimensional

Stochastic systems are infinitely dimensional and deterministic systems are low dimensional, while real systems lie somewhere between these two limit cases. If the calculation of a low (finite) dimension is in fact possible, one could conclude that the system under study is not purely random. In the present work we calculate the maximal Lyapunov exponent from interspike intervals time series recorded from the internal segment of the Globus Pallidusfrom patients with Parkinson's disease. We show the convergence of the maximal Lyapunov exponent at a dimension equal to 7 or 8, which is therefore our estimation of the embedding dimension for the system. For dimensions below 7 the observed behavior is what would be expected from a stochastic system or a complex system projecting onto lower dimensional spaces. The maximal Lyapunov exponent did not show any differences between tremor and akineto-rigid forms of the disease. However, it did decay with the value of motor Unified Parkinson's Disease Rating Scale -OFF scores. Patients with a more severe disease (higher UPDRS-OFF score) showed a lower value of the maximal Lyapunov exponent. Taken together, both indexes (the maximal Lyapunov exponent and the embedding dimension) remark the importance of taking into consideration the system's non-linear properties for a better understanding of the information transmission in the basal ganglia.

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