8,509 research outputs found
Non-intrusive and structure preserving multiscale integration of stiff ODEs, SDEs and Hamiltonian systems with hidden slow dynamics via flow averaging
We introduce a new class of integrators for stiff ODEs as well as SDEs. These
integrators are (i) {\it Multiscale}: they are based on flow averaging and so
do not fully resolve the fast variables and have a computational cost
determined by slow variables (ii) {\it Versatile}: the method is based on
averaging the flows of the given dynamical system (which may have hidden slow
and fast processes) instead of averaging the instantaneous drift of assumed
separated slow and fast processes. This bypasses the need for identifying
explicitly (or numerically) the slow or fast variables (iii) {\it
Nonintrusive}: A pre-existing numerical scheme resolving the microscopic time
scale can be used as a black box and easily turned into one of the integrators
in this paper by turning the large coefficients on over a microscopic timescale
and off during a mesoscopic timescale (iv) {\it Convergent over two scales}:
strongly over slow processes and in the sense of measures over fast ones. We
introduce the related notion of two-scale flow convergence and analyze the
convergence of these integrators under the induced topology (v) {\it Structure
preserving}: for stiff Hamiltonian systems (possibly on manifolds), they can be
made to be symplectic, time-reversible, and symmetry preserving (symmetries are
group actions that leave the system invariant) in all variables. They are
explicit and applicable to arbitrary stiff potentials (that need not be
quadratic). Their application to the Fermi-Pasta-Ulam problems shows accuracy
and stability over four orders of magnitude of time scales. For stiff Langevin
equations, they are symmetry preserving, time-reversible and Boltzmann-Gibbs
reversible, quasi-symplectic on all variables and conformally symplectic with
isotropic friction.Comment: 69 pages, 21 figure
Nonlinear dynamics of a regenerative cutting process
We examine the regenerative cutting process by using a single degree of
freedom non-smooth model with a friction component and a time delay term.
Instead of the standard Lyapunov exponent calculations, we propose a
statistical 0-1 test analysis for chaos detection. This approach reveals the
nature of the cutting process signaling regular or chaotic dynamics. For the
investigated deterministic model we are able to show a transition from chaotic
to regular motion with increasing cutting speed. For two values of time delay
showing the different response the results have been confirmed by the means of
the spectral density and the multiscaled entropy
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
Range entropy: A bridge between signal complexity and self-similarity
Approximate entropy (ApEn) and sample entropy (SampEn) are widely used for
temporal complexity analysis of real-world phenomena. However, their
relationship with the Hurst exponent as a measure of self-similarity is not
widely studied. Additionally, ApEn and SampEn are susceptible to signal
amplitude changes. A common practice for addressing this issue is to correct
their input signal amplitude by its standard deviation. In this study, we first
show, using simulations, that ApEn and SampEn are related to the Hurst exponent
in their tolerance r and embedding dimension m parameters. We then propose a
modification to ApEn and SampEn called range entropy or RangeEn. We show that
RangeEn is more robust to nonstationary signal changes, and it has a more
linear relationship with the Hurst exponent, compared to ApEn and SampEn.
RangeEn is bounded in the tolerance r-plane between 0 (maximum entropy) and 1
(minimum entropy) and it has no need for signal amplitude correction. Finally,
we demonstrate the clinical usefulness of signal entropy measures for
characterisation of epileptic EEG data as a real-world example.Comment: This is the revised and published version in Entrop
A new framework for extracting coarse-grained models from time series with multiscale structure
In many applications it is desirable to infer coarse-grained models from
observational data. The observed process often corresponds only to a few
selected degrees of freedom of a high-dimensional dynamical system with
multiple time scales. In this work we consider the inference problem of
identifying an appropriate coarse-grained model from a single time series of a
multiscale system. It is known that estimators such as the maximum likelihood
estimator or the quadratic variation of the path estimator can be strongly
biased in this setting. Here we present a novel parametric inference
methodology for problems with linear parameter dependency that does not suffer
from this drawback. Furthermore, we demonstrate through a wide spectrum of
examples that our methodology can be used to derive appropriate coarse-grained
models from time series of partial observations of a multiscale system in an
effective and systematic fashion
Scaling Behaviour and Complexity of the Portevin-Le Chatelier Effect
The plastic deformation of dilute alloys is often accompanied by plastic
instabilities due to dynamic strain aging and dislocation interaction. The
repeated breakaway of dislocations from and their recapture by solute atoms
leads to stress serrations and localized strain in the strain controlled
tensile tests, known as the Portevin-Le Chatelier (PLC) effect. In this present
work, we analyse the stress time series data of the observed PLC effect in the
constant strain rate tensile tests on Al-2.5%Mg alloy for a wide range of
strain rates at room temperature. The scaling behaviour of the PLC effect was
studied using two complementary scaling analysis methods: the finite variance
scaling method and the diffusion entropy analysis. From these analyses we could
establish that in the entire span of strain rates, PLC effect showed Levy walk
property. Moreover, the multiscale entropy analysis is carried out on the
stress time series data observed during the PLC effect to quantify the
complexity of the distinct spatiotemporal dynamical regimes. It is shown that
for the static type C band, the entropy is very low for all the scales compared
to the hopping type B and the propagating type A bands. The results are
interpreted considering the time and length scales relevant to the effect.Comment: 35 pages, 6 figure
Multiscale Turbulence Models Based on Convected Fluid Microstructure
The Euler-Poincar\'e approach to complex fluids is used to derive multiscale
equations for computationally modelling Euler flows as a basis for modelling
turbulence. The model is based on a \emph{kinematic sweeping ansatz} (KSA)
which assumes that the mean fluid flow serves as a Lagrangian frame of motion
for the fluctuation dynamics. Thus, we regard the motion of a fluid parcel on
the computationally resolvable length scales as a moving Lagrange coordinate
for the fluctuating (zero-mean) motion of fluid parcels at the unresolved
scales. Even in the simplest 2-scale version on which we concentrate here, the
contributions of the fluctuating motion under the KSA to the mean motion yields
a system of equations that extends known results and appears to be suitable for
modelling nonlinear backscatter (energy transfer from smaller to larger scales)
in turbulence using multiscale methods.Comment: 1st version, comments welcome! 23 pages, no figures. In honor of
Peter Constantin's 60th birthda
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