324 research outputs found
Non Asymptotic Properties of Transport and Mixing
We study relative dispersion of passive scalar in non-ideal cases, i.e. in
situations in which asymptotic techniques cannot be applied; typically when the
characteristic length scale of the Eulerian velocity field is not much smaller
than the domain size. Of course, in such a situation usual asymptotic
quantities (the diffusion coefficients) do not give any relevant information
about the transport mechanisms. On the other hand, we shall show that the
Finite Size Lyapunov Exponent, originally introduced for the predictability
problem, appears to be rather powerful in approaching the non-asymptotic
transport properties. This technique is applied in a series of numerical
experiments in simple flows with chaotic behaviors, in experimental data
analysis of drifter and to study relative dispersion in fully developed
turbulence.Comment: 19 RevTeX pages + 8 figures included, submitted on Chaos special
issue on Transport and Mixin
Predictability: a way to characterize Complexity
Different aspects of the predictability problem in dynamical systems are
reviewed. The deep relation among Lyapunov exponents, Kolmogorov-Sinai entropy,
Shannon entropy and algorithmic complexity is discussed. In particular, we
emphasize how a characterization of the unpredictability of a system gives a
measure of its complexity. Adopting this point of view, we review some
developments in the characterization of the predictability of systems showing
different kind of complexity: from low-dimensional systems to high-dimensional
ones with spatio-temporal chaos and to fully developed turbulence. A special
attention is devoted to finite-time and finite-resolution effects on
predictability, which can be accounted with suitable generalization of the
standard indicators. The problems involved in systems with intrinsic randomness
is discussed, with emphasis on the important problems of distinguishing chaos
from noise and of modeling the system. The characterization of irregular
behavior in systems with discrete phase space is also considered.Comment: 142 Latex pgs. 41 included eps figures, submitted to Physics Reports.
Related information at this http://axtnt2.phys.uniroma1.i
Quantum dynamical phase transition in a system with many-body interactions
We introduce a microscopic Hamiltonian model of a two level system with
many-body interactions with an environment whose excitation dynamics is fully
solved within the Keldysh formalism. If a particle starts in one of the states
of the isolated system, the return probability oscillates with the Rabi
frequency . For weak interactions with the environment
we find a slower oscillation whose
amplitude decays with a decoherence rate . However, beyond a finite critical interaction with the environment,
, the decoherence rate becomes
. The oscillation
period diverges showing a \emph{quantum dynamical phase transition}to a Quantum
Zeno phase.Comment: 5 pages, 3 figures, minor changes, fig.2 modified, added reference
Chaos and chaotic phase mixing in cuspy triaxial potentials
This paper investigates chaos and chaotic phase mixing in triaxial Dehnen
potentials which have been proposed to describe realistic ellipticals. Earlier
work is extended by exploring the effects of (1) variable axis ratios, (2)
`graininess' associated with stars and bound substructures, idealised as
friction and white noise, and (3) large-scale organised motions presumed to
induce near-random forces idealised as coloured noise with finite
autocorrelation time. Three important conclusions are: (1) not all the chaos
can be attributed to the cusp; (2) significant chaos can persist even for
axisymmetric systems; and (3) introducing a supermassive black hole can
increase both the relative number of chaotic orbits and the size of the largest
Lyapunov exponent. Sans perturbations, distribution functions associated with
initially localised chaotic ensembles evolve exponentially towards a nearly
time-independent form at a rate L that correlates with the finite time Lyapunov
exponents associated with the evolving orbits. Perturbations accelerate phase
space transport by increasing the rate of phase mixing in a given phase space
region and by facilitating diffusion along the Arnold web that connects
different phase space regions, thus facilitating an approach towards a true
equilibrium. The details of the perturbation appear unimportant. All that
matters are the amplitude and the autocorrelation time, upon which there is a
weak logarithmic dependence. Even comparatively weak perturbations can increase
L by a factor of three or more, a fact that has potentially significant
implications for violent relaxation.Comment: 17 pages, 17 figures -- revised and extended manuscript to appear in
Monthly Notices of the Royal Astronomical Societ
Modularity and the spread of perturbations in complex dynamical systems
We propose a method to decompose dynamical systems based on the idea that
modules constrain the spread of perturbations. We find partitions of system
variables that maximize 'perturbation modularity', defined as the
autocovariance of coarse-grained perturbed trajectories. The measure
effectively separates the fast intramodular from the slow intermodular dynamics
of perturbation spreading (in this respect, it is a generalization of the
'Markov stability' method of network community detection). Our approach
captures variation of modular organization across different system states, time
scales, and in response to different kinds of perturbations: aspects of
modularity which are all relevant to real-world dynamical systems. It offers a
principled alternative to detecting communities in networks of statistical
dependencies between system variables (e.g., 'relevance networks' or
'functional networks'). Using coupled logistic maps, we demonstrate that the
method uncovers hierarchical modular organization planted in a system's
coupling matrix. Additionally, in homogeneously-coupled map lattices, it
identifies the presence of self-organized modularity that depends on the
initial state, dynamical parameters, and type of perturbations. Our approach
offers a powerful tool for exploring the modular organization of complex
dynamical systems
Chaotic Disintegration of the Inner Solar System
On timescales that greatly exceed an orbital period, typical planetary orbits
evolve in a stochastic yet stable fashion. On even longer timescales, however,
planetary orbits can spontaneously transition from bounded to unbound chaotic
states. Large-scale instabilities associated with such behavior appear to play
a dominant role in shaping the architectures of planetary systems, including
our own. Here we show how such transitions are possible, focusing on the
specific case of the long-term evolution of Mercury. We develop a simple
analytical model for Mercury's dynamics and elucidate the origins of its short
term stochastic behavior as well as of its sudden progression to unbounded
chaos. Our model allows us to estimate the timescale on which this transition
is likely to be triggered, i.e. the dynamical lifetime of the Solar System as
we know it. The formulated theory is consistent with the results of numerical
simulations and is broadly applicable to extrasolar planetary systems dominated
by secular interactions. These results constitute a significant advancement in
our understanding of the processes responsible for sculpting of the dynamical
structures of generic planetary systems.Comment: 18 pages, 7 figures, accepted to Ap
๊ณ ์ฐจ์ ๋ก๋ ์ธ ์์คํ , ๋๊ธฐ์์ธก์ฑ ๋ฐ ์๋ฃ๋ํ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ์์ฐ๊ณผํ๋ํ ์ง๊ตฌํ๊ฒฝ๊ณผํ๋ถ, 2021.8. ๋ฌธ์น์ฃผ.๋ก๋ ์ธ ์์คํ
์ ๋ ์ผ๋ฆฌ ๋ฒ ๋๋ฅด ๋๋ฅ ํ์์ ๋จ์ํ ๋ชจํ์ผ๋ก ์ฒ์ ๊ณ ์๋์์ผ๋, ์ดํ ์ผ๋ฆํ ๋๊ฐ์ ๋ฐ๊ฒฌ ๋ฐ ํผ๋ ์ด๋ก ์ ๊ธ์ํ ๋ฐ์ ์ ๋ํ ๊ธฐ์ฌ ๋ฑ์ ํตํด ๊ทธ ์ค์์ฑ์ด ๊พธ์คํ ๋ถ๊ฐ๋์ด ์๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ๋ ๊ฐ์ง ์ ๊ทผ ๋ฐฉ์์ ํตํด ๋ก๋ ์ธ ์์คํ
์ ๊ณ ์ฐจ์์ผ๋ก ํ์ฅํ๊ณ ์ ํ์๋ค. ์ฒซ๋ฒ์งธ ์ ๊ทผ ๋ฐฉ์์ ์ ๋ ๊ณผ์ ์์ ๋น๋กฏ๋๋ ํธ๋ฆฌ์ ๊ธ์์ ์ ๋จ์ ์์ด ์ถ๊ฐ ๋ชจ๋๋ฅผ ํตํด ์ฐจ์๋ฅผ ํ์ฅํ๋ ๋ฐฉ๋ฒ์ผ๋ก, ๋ณธ ์ฐ๊ตฌ์์๋ ์ด๋ฅผ ์ผ๋ฐํ ํ์ฌ ์์์ ์์ฐ์ ์ ๋ํ ๋ฐ ์ฐจ ๋ก๋ ์ธ ์์คํ
์ ์ ๋ํ์๋ค. ๋๋ฒ์งธ๋ ๋ฌผ๋ฆฌ์ ํ์ฅ์ด๋ผ ๋ถ๋ฆฌ๋ ๋ฐฉ์์ผ๋ก, ๋ ์ผ๋ฆฌ ๋ฒ ๋๋ฅด ๋๋ฅ ํ์์ ๊ด์ฅํ๋ ์ง๋ฐฐ๋ฐฉ์ ์์ ๋ํ๋ด๊ณ ์ ํ๋ ๋ฌผ๋ฆฌ ์ฑ๋ถ์ ์ถ๊ฐํ์ฌ ๋ ๋์ ์ฐจ์์ ๋ฐฉ์ ์๊ณ๋ฅผ ์ป๋ ๊ณผ์ ์ด๋ค. ์ด์ ์ถ๊ฐ ๋ฌผ๋ฆฌ ์ฑ๋ถ์ผ๋ก ๋ชจํ ํ๋ ์์ ํ์ ๊ณผ ๋ด๋ถ์ ๋ถ์ ํ๋ ์ค์ผ ๋ฌผ์ง ๋ฐ์์ ์ค์นผ๋ผ๋ฅผ ๊ณ ๋ คํ์ฌ ์๋ก์ด 6์ฐจ ๋ก๋ ์ธ ์์คํ
์ ์ ๋ ํ ์ ์์๋ค. ์ด๋ ๊ฒ ์ป์ด์ง ๊ณ ์ฐจ์ ๋ก๋ ์ธ ์์คํ
์ ๋น์ ํ์ฑ, ๋์นญ์ฑ, ์์ฐ์ฑ ๋ฑ์ ๊ณตํต๋ ํน์ง์ ์ง๋๋ค.
์๋กญ๊ฒ ํ์ฅ๋ ๋ก๋ ์ธ ์์คํ
์ ํด์ ํน์ฑ ๋ฐ ๊ทธ๋ค์ด ๋ํ๋ด๋ ๋ค์ํ ๋น์ ํ ํ์์ ๊ท๋ช
์ ์์น ์ ๋ถ์ ํตํด ์ป์ ํด์ ๋ถ์์ ๋ฐํ์ผ๋ก ์ด๋ฃจ์ด์ก๋ค. ์ด๋ฅผ ์ํด ์นด์ค์ค ์ด๋ก ์ ์
๊ฐํ ์ฌ๋ฌ๊ฐ์ง ๋ถ์ ๋ฐฉ๋ฒ์ด ํ์ฉ๋์๋๋ฐ, ์ด๋ฌํ ๋ถ์๋ฐฉ๋ฒ์๋ ํ๋ผ๋ฏธํฐ ๊ณต๊ฐ ์์ ์ฃผ๊ธฐ์ฑ๋ํ, ๋ถ๊ธฐ๋ํ ๋ฐ ๋ฆฌ์ํธ๋
ธํ ์ง์ ๊ทธ๋ฆฌ๊ณ ์์ ๊ณต๊ฐ ๋ด ํด์ ๊ถค๋ ๋ฐ ํ๋ ํ ํก์ธ๊ฒฝ๊ณ ๋ฑ์ด ์๋ค. ๋ฐํ์ง ๋น์ ํ ๋์ญํ์ ํ์ ์ค ํนํ ์ฃผ๋ชฉํ ๋งํ ํ์์๋ ํ๋ผ๋ฏธํฐ ๊ฐ์ ๋ฐ๋ฅธ ๋ถ๊ธฐ ๊ตฌ์กฐ์ ๋ณ๋, ํ๋์ ์์ ๊ณต๊ฐ ๋ด ์กด์ฌํ๋ ์ฌ๋ฌ ํ์
์ ํด์ ๊ณต์กด, ์นด์ค์ค์ ๋๊ธฐํ ๋ฑ์ด ์๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ์ด๋ฌํ ํ์์ ์ํ์ ~~์์น์ ๋ถ์๋ฟ๋ง ์๋๋ผ ์ด๊ฒ์ด ๋๊ธฐ๊ณผํ ํนํ ์๋ฃ๋ํ์ ๋๊ธฐ์์ธก์ฑ ๋ถ์ผ์ ํจ์ํ๋ ๋ฐ๊ฐ ๋ฌด์์ธ์ง๋ ํ๊ตฌํ์๋ค.
๋ณธ ์ฐ๊ตฌ์์ ์ ์๋ ์ผ๋ฐํ ๋ฐฉ์์ ๋ฐ๋ผ ๋ก๋ ์ธ ์์คํ
์ ์ฐจ์๋ฅผ ์ฌ๋ฆฌ๋ฉด ๋ถ๊ธฐ ๊ตฌ์กฐ์ ๋ณ๋์ด ์ผ์ด๋ ์๊ณ ๋ ์ผ๋ฆฌ ํ๋ผ๋ฏธํฐ์ ์ฆ๊ฐ๊ฐ ๋น๋กฏ๋๋ค. ์ฌ๊ธฐ์ ์๊ณ ๋ ์ผ๋ฆฌ ํ๋ผ๋ฏธํฐ๋ ์นด์ค์ค๊ฐ ์ฒ์ ๋ฐ์ํ๋ ๊ฐ์ฅ ๋ฎ์ ๋ ์ผ๋ฆฌ ํ๋ผ๋ฏธํฐ ๊ฐ์ด๋ฏ๋ก ์ด๊ฒ์ด ์ฐจ์์ ๋ฐ๋ผ ์ฆ๊ฐํ๋ค๋ ๊ฒ์ ์ฆ ๊ณ ์ฐจ์ ๋ก๋ ์ธ ์์คํ
์์๋ ์นด์ค์ค์ ๋ฐ์์ด ์ ์ฐจ์์์๋ณด๋ค ๋ ์ด๋ ต๋ค๋ ๊ฒ์ ์๋ฏธํ๋ค. ์ฐจ์ ๋ฐ ํ๋ผ๋ฏธํฐ ๊ณต๊ฐ์ ๊ทธ๋ ค์ง ์ฃผ๊ธฐ์ฑ ๋ํ๋ฅผ ๋ณด๋ฉด ์นด์ค์ค๊ฐ ์กด์ฌํ๋ ์์ญ์ด ์ฐจ์์ ๋ฐ๋ผ ์ ์ ์ค์ด๋ค๊ณ , ์ด๋ ์ฐจ์ ์ด์๋ถํฐ๋ ์ฌ๋ผ์ง๋ ๊ฒ์ ํ์ธ ํ ์ ์๋ค. ๋ง์ฐฌ๊ฐ์ง๋ก ๋ฌผ๋ฆฌ์ ์ผ๋ก ํ์ฅ๋ ๋ก๋ ์ธ ์์คํ
์์๋ ์๊ณ ๋ ์ผ๋ฆฌ ํ๋ผ๋ฏธํฐ๊ฐ ์ถ๊ฐ๋ ๋ฌผ๋ฆฌํ์์ ๋ํ๋ด๋ ์๋ก์ด ํ๋ผ๋ฏธํฐ์ ๊ฐ์ ์ฆ๊ฐ์ํด์ ๋ฐ๋ผ ์ฆ๊ฐํ๋ ๊ฒ์ ์ ์ ์๋ค. ํํธ ์ ์ฒด ๋ด ์ค์นผ๋ผ ํจ๊ณผ์ ์ฐ๊ด๋ ํ๋ผ๋ฏธํฐ๋ง ์ ์ง์ ์ผ๋ก ์ฌ๋ฆด ๊ฒฝ์ฐ์๋ ์์คํ
์ ๋ถ์์ ์ ์ผ๊ธฐํ๋ ๋ ์ผ๋ฆฌ ํ๋ผ๋ฏธํฐ์ ์์ ์ ์ผ๊ธฐํ๋ ์ค์นผ๋ผ ๊ด๋ จ ํ๋ผ๋ฏธํฐ ๊ฐ์ ๊ฒฝ์์ผ๋ก ์ธํด ์์คํ
์ด ์์ ํ ์์ ํ ๋๊ธฐ ์ ์นด์ค์ค ํด๊ฐ ํ๋ฒ ๋ ๋ฐ์ํ๋ ํ์์ด ์ผ์ด๋๋ค. ์ด ๋๋ฒ์งธ ์นด์ค์ค์ ๋์๋๋ ๋๊ฐ๋ ๊ธฐ์กด์ ์๋ ค์ง ๋ก๋ ์ธ ๋๊ฐ์๋ ์ฌ๋ญ ๋ค๋ฅธ ๋ชจ์์๋ฅผ ๋ณด์ธ๋ค.
ํด์ ๊ณต์กด ํ์์ ๋ก๋ ์ธ ์ ์ํด ๋ฐํ์ง ํด์ ์ด๊ธฐ์กฐ๊ฑด์ ๋ํ ๋ฏผ๊ฐ๋์๋ ๊ตฌ๋ถ๋๋ ๊ฐ๋
์ผ๋ก, ์ด๊ธฐ์กฐ๊ฑด์ผ๋ก ์ธํ ์นด์ค์ค ํด ๊ฐ์ ์ฐจ์ด๊ฐ ์ฆํญ๋๋ ์ด๋ฅธ๋ฐ ๋๋นํจ๊ณผ์๋ ๋ฌ๋ฆฌ ์ด๊ธฐ์กฐ๊ฑด์ ๋ฐ๋ผ ์์ ํ ๋ค๋ฅธ ํ์
์ ํด๋ฅผ ๋ํ๋ด๋ ๋๊ฐ๊ฐ ๊ฐ์ ์์๊ณต๊ฐ์ ๊ณต์กดํจ์ ์๋ฏธํ๋ค. ๋ฐ๋ผ์ ๋ง์ฝ ์ค์ ๋ ์จ๋ฅผ ๋ํ๋ด๋ ์์คํ
์ด ์์กดํ๋ ์์๊ณต๊ฐ์์ ์ด๋ฌํ ํด์ ๊ณต์กด์ด ์ค์ ํ๋ค๋ฉด ์ด๊ฒ์ ์นด์ค์ค์ ์ด๊ธฐ์กฐ๊ฑด์ ๋ํ ๋ฏผ๊ฐ์ฑ๊ณผ ๋๋ถ์ด ๋๊ธฐ์์ธก์ฑ ํนํ ์์๋ธ ์๋ณด์ ์ด๋ก ์ ์ผ๋ก ์์ฌํ๋ ๋ฐ๊ฐ ํด ๊ฒ์ผ๋ก ์๊ฐ๋๋ค. ๋ฌผ๋ฆฌ์ ์ผ๋ก ํ์ฅ๋ ๋ก๋ ์ธ ์์คํ
์์๋ ๊ธฐ์กด ๋ก๋ ์ธ ์์คํ
๊ณผ ๊ฐ์ด ๋ ์ผ๋ฆฌ ํ๋ผ๋ฏธํฐ์ ๋ณํ์ ๋ฐ๋ฅธ ๋ถ๊ธฐ ๊ตฌ์กฐ์ ๋ถ์ ํฉ์ผ๋ก ์ธํด ๋น๋กฏ๋๋ ํด์ ๊ณต์กด์ด ๋ํ๋๋ค. ํด์ ๊ณต์กด ๊ฐ๋ฅ์ฑ์ด ๋์ ํ๋ผ๋ฏธํฐ ์กฐํฉ์ ์ฐพ์๋ด๊ธฐ ์ํด ๋ฌผ๋ฆฌ์ ์ผ๋ก ํ์ฅ๋ 6์ฐจ ๋ก๋ ์ธ ์์คํ
์ ๋ถ๊ธฐ๊ตฌ์กฐ๋ฅผ ์์น์ ~~ํด์์ ๋ฐฉ๋ฒ์ผ๋ก ๋์ถํ์๊ณ ์ด๊ธฐ์กฐ๊ฑด์ ๋ฐ๋ผ ์๋ก ๋ค๋ฅธ ๋๊ฐ์ง ์ข
๋ฅ์ ๋ถ๊ธฐ ์ฆ ํธํ ๋ฐ ํคํ
๋กํด๋ฆฌ๋ ๋ถ๊ธฐ๊ฐ ์๊ฐ๋ฆฌ๋ ๊ตฌ๊ฐ์ ์ง์ค์ ์ผ๋ก ๋ถ์ํ์๋ค.
๊ธฐ์กด 3์ฐจ ๋ก๋ ์ธ ์์คํ
์์ ํ๋์ ๋ณ์์ ๋ํ ์ ๋ณด ์ ๋ฌ ๋ง์ผ๋ก๋ ์๊ธฐ๋๊ธฐํ ํ์์ด ์ผ์ด๋จ์ ์ด๋ฏธ ์ ์๋ ค์ง ์ฌ์ค์ด๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ๋ฌผ๋ฆฌ์ ์ผ๋ก ํ์ฅ๋ ๋ก๋ ์ธ ์์คํ
์์๋ ๊ธฐ์กด ๋ก๋ ์ธ ์์คํ
๊ณผ ๊ฐ์ ์กฐ๊ฑด ํ์์ ์นด์ค์ค์ ์๊ธฐ๋๊ธฐํ๊ฐ ์ผ์ด๋๋ ์ ์ ์ ์ ํ ๋ฆฌ์ํธ๋
ธํ ํจ์์ ์ ์๋ฅผ ํตํด ์ฆ๋ช
ํ์๋ค. ์ผ๋ฐํ๋ ๋ก๋ ์ธ ์์คํ
์ ์๊ธฐ๋๊ธฐํ์ ๋ํด์๋ ๋น๋ก ์ํ์ ์ฆ๋ช
์ด ๋๋ฐ๋์ง๋ ์์์ง๋ง ์์น์ ๋ฐฉ๋ฒ์ ํตํด ์ญ์ ๊ฐ์ ์กฐ๊ฑด ํ์์ ์๊ธฐ๋๊ธฐํ๊ฐ ์ผ์ด๋จ์ ๋ท๋ฐ์นจ ํ ๊ทผ๊ฑฐ๋ฅผ ์ ์ํ์๋ค. ๋ํ ์์น ์คํ์ ํตํด ์๋ก ๋ค๋ฅธ ์ฐจ์๋ฅผ ๊ฐ์ง ์ผ๋ฐํ๋ ๋ก๋ ์ธ ์์คํ
๊ฐ ๋๊ธฐํ๊ฐ ์ผ์ด๋๋ ์ ๋๊ฐ ์ํธ ์ฐจ์๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ํ ๋ ์์คํ
์ฌ์ด์ ๊ฑฐ๋ฆฌ์ ์์ ์๊ด๊ด๊ณ๋ฅผ ๊ฐ์ง๋ค๋ ์ ๋ ํ์ธํ์๋ค.
์ถ๊ฐ ํธ๋ฆฌ์ ๋ชจ๋๋ฅผ ํฌํจํ์ฌ ๋ ์์ ์ค์ผ์ผ์ ์ด๋์ ๋ถํดํ ์ ์๋ ๊ณ ์ฐจ์ ๋ก๋ ์ธ ์์คํ
๊ณผ ๊ทธ๋ ๊ฒ ํ์ง ๋ชปํ๋ ์ ์ฐจ์ ๋ก๋ ์ธ ์์คํ
๊ฐ ๋๊ธฐํ์ ๊ฐ๋ฅ์ฑ์ ๋๊ธฐ๊ณผํ์์ ํนํ ๋๊ธฐ ๋ชจํ ๋ฐ ์๋ฃ๋ํ์ ์์ด ์ค์ํ ๊ฐ๋
์ ์ธ ํจ์๋ฅผ ๊ฐ์ง๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ํน๋ณํ ์์๋ธ ์นผ๋ง ํํฐ ์๋ฃ๋ํ ๊ธฐ๋ฒ์ ์ผ๋ก๋ก ์ผ๋ฐํ๋ ๋ก๋ ์ธ ์์คํ
์ด ์๋ฃ๋ํ ๊ธฐ๋ฒ์ ๋น๊ต์ ๋จ์ํ ํ
์คํธ๋ฒ ๋๋ก์จ์ ์ญํ ์ ํ ์ ์๋์ง ํ๊ตฌํ์๋ค. ์นด์ค์ค ๋๊ธฐํ ํ์์ ๊ธฐ๋ฐ์ ๋ ๊ฐ๋
์ ๋์์ผ๋ก ๋ฐ์ ์๋ฅผ ์ค์ ๋๊ธฐ ํ์, ์์ ์๋ฅผ ๋๊ธฐ ๋ชจํ, ๊ทธ๋ฆฌ๊ณ ๋ฐ์ ์์์ ์์ ์๋ก ์ ๋ฌ๋๋ ์ ๋ณด๋ฅผ ๊ด์ธก์ ๋์์ํด์ผ๋ก์จ ์์ ์์ ๋ฐ์ ์ ๊ฐ์ ์ค์ฐจ, ๋ฐ์ ์์์ ์์ ์๋ก ์ ๋ฌํ ์ ๋ณด ์ถ์ถ ๊ณผ์ ์์ ๋น๋กฏ๋๋ ์ค์ฐจ ๋ฑ์ ํตํด ์ค์ ๋๊ธฐ ๋ชจํ๊ณผ ๊ด์ธก์ ๋ถ์์ ํจ์ ๊ฐ๋
์ ์ผ๋ก ๋ชจ์ํ ์ ์์๋ค.
์ผ๋ฐํ๋ ๋ก๋ ์ธ ์์คํ
์์ ์ด๊ธฐ์กฐ๊ฑด์ ์์ฃผ ์์ ์ญ๋์ ์ค ํด์ ๊ทธ๋ ์ง ์์ ํด ๊ฐ์ ๋น๊ต๋ฅผ ํตํด ์ด๊ฒ์ด ๋๊ธฐ์์ธก์ฑ์ ํจ์ํ๋ ๋ฐ๊ฐ ๋ฌด์์ธ์ง ํ๊ตฌํ์๋ค. ์ด๋ ์ด๋ ๊ฒ ๋ ํด๊ฐ ๋ฒ์ด์ง๋ ์ ๋๊ฐ ๊ธฐ์ค๊ฐ์ ๋๊ฒ ๋๋ ์๊ฐ์ ํธ์ฐจ์๊ฐ์ด๋ผ ์นญํ๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ํธ์ฐจ์๊ฐ์ด ์ ์ด๋ ์ฃผ์ด์ง ํ๋ผ๋ฏธํฐ ๊ฐ ํ์์๋ ๋ก๋ ์ธ ์์คํ
์ ์ฐจ์์ ๋ํ ๊ฐํ ๋น๋จ์กฐ์ ์์กด์ฑ์ ๋ณด์์ ๋ฐ๊ฒฌํ์๋ค. ๋ํ ์ด๋ ๊ฒ ์ ์๋ ํธ์ฐจ์๊ฐ์ ํ์ฉํ์ฌ ์ค์ ๋ ์จ ์ฌ๋ก์ ์์น ์๋ณด ๋ชจ์์์ ๋ํ๋๋ ๋๊ธฐ์์ธก์ฑ์ ์ธก์ ํ์์๋, ๋ง์ฐฌ๊ฐ์ง๋ก ๋๊ธฐ์์ธก์ฑ์ด ์ฐ์งํด์๋์ ๋ํ ๋น๋จ์กฐ์ ์์กด์ฑ์ ๋ณด์ด๋ ๊ฒ์ ํ์ธํ ์ ์์๋ค. ์ด์ ์ด๋ฌํ ๋น๋จ์กฐ์ ์์กด์ฑ์ ๊ทผ๋ณธ์ ์ธ ์์ธ์ ๋ชจํ์ ๋๊ธฐ ๋์๊ฐ ์ค์ ๋ ์จ์ ๋ด์ฌ๋ ์นด์ค์ค์ ์์ ์ ์์์ ์ ์ํ์๋ค.The Lorenz system is a simplified model of Rayleigh--B\'{e}nard convection whose importance lies not only in understanding the fluid convection problem but also in its formative role in the discovery of strange attractors and the subsequent development of the modern theory of chaos. In this dissertation, two different approaches to extending the Lorenz system to higher dimensions are considered. First, by including additional wavenumber modes at the series truncation stage of the derivation, the so-called high-order Lorenz systems are obtained up to dimension 11, which are then generalized into and dimensions for any positive integer . Second, by incorporating additional physical ingredients, namely, rotation and density-affecting scalar in the governing equations, a new 6-dimensional physically extended Lorenz system is derived. All of these high-dimensional extensions of the Lorenz system are shown to share some basic properties such as nonlinearity, symmetry, and volume contraction.
The numerically obtained solutions of the extended Lorenz systems are studied through periodicity diagrams, bifurcation diagrams, and Lyapunov exponent spectra in parameter spaces and also through solution trajectories and basin boundaries in the phase space, illuminating various nonlinear dynamical phenomena such as shifts in the bifurcation structures, attractor coexistence, and chaos synchronization. Accompanying these results are discussions about their applicability and theoretical implications, particularly in the context of data assimilation and atmospheric predictability.
The shifts in bifurcation structures induced by raising the dimension lead to higher critical Rayleigh parameter values, implying that it gets more difficult for chaos to emerge at higher dimensions. Periodicity diagrams reveal that the parameter ranges in which chaos resides tend to diminish with rising dimensions, eventually vanishing altogether. Likewise, simultaneously increasing the newly added parameters in the physically extended Lorenz system leads to higher critical Rayleigh parameter values; however, raising only the scalar-related parameter leads to an eventual return of chaos albeit with an attractor with qualitatively distinct features from the Lorenz attractor. The peculiar bifurcation structure shaped by the competition between the opposing effects of raising the Rayleigh and the scalar-related parameters helps explain this second onset of chaos.
Attractor coexistence refers to the partition of the phase space by basin boundaries so that different types of attractors emerge depending on the initial condition. Similar to the original Lorenz system, the physically extended Lorenz system is found to exhibit attractor coexistence stemming from mismatches between the Hopf and heteroclinic bifurcations. If the atmosphere is found to exhibit such behavior, it can have grave implications for atmospheric predictability and ensemble forecasting beyond mere sensitive dependence on initial conditions, which only applies to chaotic solutions.
Chaos synchronization is another curious phenomenon known to occur in the Lorenz system. By finding an appropriate Lyapunov function, the physically extended Lorenz system is shown to self-synchronize under the same condition that guarantees self-synchronization in the original Lorenz system. Regarding the generalized Lorenz systems, numerical evidence in support of self- as well as some degree of generalized synchronization, that is, synchronization between two Lorenz systems differing in their dimensions, is provided. Numerical results suggest that the smaller the dimensional difference between the two, the stronger they tend to synchronize.
Some conceptual implications of such results are discussed in relation to atmospheric modeling and data assimilation. Especially, the feasibility of using the -dimensional Lorenz systems as a testbed for data assimilation methods is explored. For demonstration, the ensemble Kalman filter method is implemented to assimilate observations with ensembles of model outputs generated using the generalized Lorenz systems, whose imperfections are simulated through varying the severity of ensemble over- or underdispersion, dimensional differences, random forcing, and model or observation biases.
Further investigation of the generalized Lorenz systems is carried out from the perspective of predictability, showing that predictability measured by deviation time, which is the time when the threshold-exceeding deviations among ensemble members occur, can respond non-monotonically to increases in the system's dimension. Accordingly, deviation time is put forward as a direct measure of predictability due to weather's sensitive dependence on initial conditions. Raising the dimension under the proposed generalizations is thought to be analogous to resolving smaller-scale motions in the vertical direction. The estimated deviation times in an ensemble of real-case simulations using a realistic numerical weather forecasting model reveal that the predictability of real-case simulations also depend non-monotonically on model vertical resolution. It is suggested that beneath this non-monotonicity fundamentally lies chaos inherent to the model atmospheres and, by extension, weather at large.1 Overview 1
1.1 Chaos and the Lorenz system 1
1.2 Extending the Lorenz system 6
1.3 Bifurcations and related phenomena 8
1.4 Chaos in the atmosphere 14
1.5 Organization of the dissertation 16
2 Chaos and Periodicity of the High-Order Lorenz Systems 18
2.1 Introduction 18
2.2 The high-order Lorenz systems 20
2.2.1 Derivation 22
2.2.2 Some properties of the Lorenz systems 24
2.3 Numerical methods 26
2.4 Results 32
2.4.1 Periodicity diagrams 32
2.4.2 Bifurcation diagrams and phase portraits 34
2.5 Discussion 40
3 A Physically Extended Lorenz System with
Rotation and Density-Affecting Scalar 42
3.1 Introduction 42
3.2 Derivation 45
3.3 Effects of rotation and scalar 49
3.3.1 Fixed points and stability 49
3.3.2 Bifurcation structure in the rT-ฯ space 52
3.3.3 Bifurcations along rC and s 55
3.4 The case when ฮฒ < 0 65
3.4.1 Bifurcation and the onset of chaos 67
3.4.2 Chaotic attractors and associated flow patterns 73
3.5 Self-synchronization 81
3.6 Discussion 85
4 Coexisting Attractors in the Physically Extended Lorenz System 87
4.1 Introduction 87
4.2 Methodology 89
4.3 Results 92
4.3.1 Coexisting attractors in the LorenzStenflo system 92
4.3.2 Coexisting attractors under rotation and scalar 100
4.4 Discussion 110
5 The (3N)- and (3N + 2)-Dimensional Generalizations of the Lorenz System 113
5.1 Introduction 113
5.2 The generalized Lorenz systems 115
5.2.1 The Pk- and Qk-sets for nonlinear terms 115
5.2.2 The (3N)- and (3N + 2)-dimensional systems 116
5.2.3 Choosing the nonlinear pairs 117
5.3 Derivation 119
5.3.1 The (3N)-dimensional generalization 121
5.3.2 The (3N + 2)-dimensional generalization 126
5.4 Effects of dimension in parameter spaces 126
5.4.1 Linear stability analysis 126
5.4.2 Chaos in dimension-parameter spaces 130
5.5 Perspectives on predictability 136
5.5.1 Notions of predictability 136
5.5.2 Twin experiments and deviation time 138
5.6 Discussion 144
6 Chaos Synchronization in the Generalized Lorenz Systems 147
6.1 Introduction 147
6.2 Self-synchronization 149
6.2.1 Numerical evidence 149
6.2.2 Error subsystems 155
6.3 Application in image encryption 157
6.3.1 Demonstration: A simple approach 157
6.3.2 Demonstration: An alternative approach 168
6.4 Beyond self-synchronization 172
6.5 Discussion 180
7 The Generalized Lorenz Systems as a Testbed for Data Assimilation: The Ensemble Kalman Filter 182
7.1 Introduction 182
7.2 Methodology 187
7.2.1 Implementation of the ensemble Kalman filter 188
7.3 Results 191
7.3.1 Effects of ensemble size and model accuracy 191
7.3.2 Effects of observation frequency and accuracy 205
7.3.3 Effects of observation and model biases 214
7.4 Discussion 218
8 Can Chaos Theory Explain Non-Monotonic Dependence of Atmospheric Predictability on Model Vertical Resolution 220
8.1 Introduction 220
8.2 Background 222
8.2.1 Lorenz's ideas about atmospheric predictability 222
8.2.2 Model vertical resolution and predictability in numerical weather prediction 224
8.3 Results 229
8.3.1 Deviation time in the Lorenz systems revisited 229
8.3.2 WRF model control simulations 232
8.3.3 WRF model ensemble experiments and deviation time 241
8.3.4 Spatial distribution of deviation time 254
8.4 Discussion 261
9 Summary and Final Remarks 264
Bibliography 271
Abstract in Korean 295
Acknowledgments 299
Index 303๋ฐ
- โฆ