Characterizing Small-Scale Dynamics of Navier-Stokes Turbulence with Transverse Lyapunov Exponents: A Data Assimilation Approach.

Data assimilation (DA) of turbulence, which involves reconstructing small-scale turbulent structures based on observational data from large-scale ones, is crucial not only for practical forecasting but also for gaining a deeper understanding of turbulent dynamics. We propose a theoretical framework for DA of turbulence based on the transverse Lyapunov exponents (TLEs) in synchronization theory. Through stability analysis using TLEs, we identify a critical length scale as a key condition for DA; turbulent dynamics smaller than this scale are synchronized with larger-scale turbulent dynamics. Furthermore, considering recent findings for the maximal Lyapunov exponent and its relation with the TLEs, we clarify the Reynolds number dependence of the critical length scale.

Dynamical system analysis of a data-driven model constructed by reservoir computing.

This study evaluates data-driven models from a dynamical system perspective, such as unstable fixed points, periodic orbits, chaotic saddle, Lyapunov exponents, manifold structures, and statistical values. We find that these dynamical characteristics can be reconstructed much more precisely by a data-driven model than by computing directly from training data. With this idea, we predict the laminar lasting time distribution of a particular macroscopic variable of chaotic fluid flow, which cannot be calculated from a direct numerical simulation of the Navier-Stokes equation because of its high computational cost.

Network analysis of chaotic systems through unstable periodic orbits.

A chaotic motion can be considered an irregular transition process near unstable periodic orbits embedded densely in a chaotic set. Therefore, unstable periodic orbits have been used to characterize properties of chaos. Statistical quantities of chaos such as natural measures and fractal dimensions can be determined in terms of unstable periodic orbits. Unstable periodic orbits that can provide good approximations to averaged quantities of chaos or turbulence are also known to exist. However, it is not clear what type of unstable periodic orbits can capture them. In this paper, a model for an irregular transition process of a chaotic motion among unstable periodic orbits as nodes is constructed by using a network. We show that unstable periodic orbits which have lots of links in the network tend to capture time averaged properties of chaos. A scale-free property of the degree distribution is also observed.

Time-delayed feedback control of diffusion in random walkers.

Time delay in general leads to instability in some systems, while specific feedback with delay can control fluctuated motion in nonlinear deterministic systems to a stable state. In this paper, we consider a stochastic process, i.e., a random walk, and observe its diffusion phenomenon with time-delayed feedback. As a result, the diffusion coefficient decreases with increasing delay time. We analytically illustrate this suppression of diffusion by using stochastic delay differential equations and justify the feasibility of this suppression by applying time-delayed feedback to a molecular dynamics model.

Manifold structures of unstable periodic orbits and the appearance of periodic windows in chaotic systems.

Manifold structures of the Lorenz system, the Hénon map, and the Kuramoto-Sivashinsky system are investigated in terms of unstable periodic orbits embedded in the attractors. Especially, changes of manifold structures are focused on when some parameters are varied. The angle between a stable manifold and an unstable manifold (manifold angle) at every sample point along an unstable periodic orbit is measured using the covariant Lyapunov vectors. It is found that the angle characterizes the parameter at which the periodic window corresponding to the unstable periodic orbit finishes, that is, a saddle-node bifurcation point. In particular, when the minimum value of the manifold angle along an unstable periodic orbit at a parameter is small (large), the corresponding periodic window exists near (away from) the parameter. It is concluded that the window sequence in a parameter space can be predicted from the manifold angles of unstable periodic orbits at some parameter. The fact is important because the local information in a parameter space characterizes the global information in it. This approach helps us find periodic windows including very small ones.

Covariant Lyapunov analysis of chaotic Kolmogorov flows.

Hyperbolicity is an important concept in dynamical system theory; however, we know little about the hyperbolicity of concrete physical systems including fluid motions governed by the Navier-Stokes equations. Here, we study numerically the hyperbolicity of the Navier-Stokes equation on a two-dimensional torus (Kolmogorov flows) using the method of covariant Lyapunov vectors developed by Ginelli et al. [Phys. Rev. Lett. 99, 130601 (2007)]. We calculate the angle between the local stable and unstable manifolds along an orbit of chaotic solution to evaluate the hyperbolicity. We find that the attractor of chaotic Kolmogorov flows is hyperbolic at small Reynolds numbers, but that smaller angles between the local stable and unstable manifolds are observed at larger Reynolds numbers, and the attractor appears to be nonhyperbolic at a certain Reynolds numbers. Also, we observed some relations between these hyperbolic properties and physical properties such as time correlation of the vorticity and the energy dissipation rate.

Scytale decodes chaos: a method for estimating unstable symmetric solutions.

A method for estimating a period of unstable periodic solutions is suggested in continuous dissipative chaotic dynamical systems. The measurement of a minimum distance between a reference state and an image of transformation of it exhibits a characteristic structure of the system, and the local minima of the structure give candidates of period and state of corresponding symmetric solutions. Appropriate periods and initial states for the Newton method are chosen efficiently by setting a threshold to the range of the minimum distance and the period.

Periodic-orbit determination of dynamical correlations in stochastic processes.

It is shown that the large-deviation statistical quantities of the discrete-time, finite-state Markov process P_{n+1};{(j)}= summation _{k=1};{N}H_{jk}P_{n};{(k)} , where P_{n};{(j)} is the probability for the j state at the time step n and H_{jk} is the transition probability, completely coincide with those from the Kalman map corresponding to the above Markov process. Furthermore, it is demonstrated that, by using simple examples, time correlation functions in finite-state Markov processes can be well described in terms of unstable periodic orbits embedded in the equivalent Kalman maps.

Chaotically oscillating interfaces in a parametrically forced system.

Structures and motions of a single interface exhibiting chaotic behavior are studied in the one-dimensional parametrically forced complex Ginzburg-Landau equation. There exist two kinds of chaotic interfaces whose differences are characterized by their chiral symmetry and the diffusivity of their motion. The transition between these behaviors is also investigated from the viewpoint of singularities of several dynamical variables, such as the diffusion constant, the resident time to each state, and the maximum trapping time to the unstable solution.