56,718 research outputs found
The hard-disk fluid revisited
The hard-disk model plays a role of touchstone for testing and developing the
transport theory. By large scale molecular dynamics simulations of this model,
three important autocorrelation functions, and as a result the corresponding
transport coefficients, i.e., the diffusion constant, the thermal conductivity
and the shear viscosity, are found to deviate significantly from the
predictions of the conventional transport theory beyond the dilute limit. To
improve the theory, we consider both the kinetic process and the hydrodynamic
process in the whole time range, rather than each process in a seperated time
scale as the conventional transport theory does. With this consideration, a
unified and coherent expression free of any fitting parameters is derived
succesfully in the case of the velocity autocorrelation function, and its
superiority to the conventional `piecewise' formula is shown. This expression
applies to the whole time range and up to moderate densities, and thus bridges
the kinetics and hydrodynamics approaches in a self-consistent manner.Comment: 5 pages, 4 figure
Brownian motion: from kinetics to hydrodynamics
Brownian motion has served as a pilot of studies in diffusion and other
transport phenomena for over a century. The foundation of Brownian motion, laid
by Einstein, has generally been accepted to be far from being complete since
the late 1960s, because it fails to take important hydrodynamic effects into
account. The hydrodynamic effects yield a time dependence of the diffusion
coefficient, and this extends the ordinary hydrodynamics. However, the time
profile of the diffusion coefficient across the kinetic and hydrodynamic
regions is still absent, which prohibits a complete description of Brownian
motion in the entire course of time. Here we close this gap. We manage to
separate the diffusion process into two parts: a kinetic process governed by
the kinetics based on molecular chaos approximation and a hydrodynamics process
described by linear hydrodynamics. We find the analytical solution of vortex
backflow of hydrodynamic modes triggered by a tagged particle. Coupling it to
the kinetic process we obtain explicit expressions of the velocity
autocorrelation function and the time profile of diffusion coefficient. This
leads to an accurate account of both kinetic and hydrodynamic effects. Our
theory is applicable for fluid and Brownian particles, even of irregular-shaped
objects, in very general environments ranging from dilute gases to dense
liquids. The analytical results are in excellent agreement with numerical
experiments.Comment: 8pages,3figure
Modified Stokes-Einstein Relation for Small Brownian Particles
The Stokes-Einstein (SE) relation has been widely applied to quantitatively
describe the Brownian motion. Notwithstanding, here we show that even for a
simple fluid, the SE relation may not be completely applicable. Namely,
although the SE relation could be a good approximation for a large enough
Brownian particle, we find that it induces significant error for a smaller
Brownian particle, and the error increases with the decrease of the Brownian
particle's size, till the SE relation fails completely when the size of
Brownian particle is comparable with that of a fluid molecule. The cause is
rooted in the fact that the kinetic and the hydrodynamic effects depend on the
size of the Brownian particle differently. By excluding the kinetic
contribution to the diffusion coefficient, we propose a revised Stokes-Einstein
relation and show that it expands significantly the applicable range.Comment: 3 figure
Copy the dynamics using a learning machine
Is it possible to generally construct a dynamical system to simulate a black
system without recovering the equations of motion of the latter? Here we show
that this goal can be approached by a learning machine. Trained by a set of
input-output responses or a segment of time series of a black system, a
learning machine can be served as a copy system to mimic the dynamics of
various black systems. It can not only behave as the black system at the
parameter set that the training data are made, but also recur the evolution
history of the black system. As a result, the learning machine provides an
effective way for prediction, and enables one to probe the global dynamics of a
black system. These findings have significance for practical systems whose
equations of motion cannot be approached accurately. Examples of copying the
dynamics of an artificial neural network, the Lorenz system, and a variable
star are given. Our idea paves a possible way towards copy a living brain.Comment: 8 pages, 4 figure
Inferring Global Dynamics of a Black-Box System Using Machine Learning
We present that, instead of establishing the equations of motion, one can
model-freely reveal the dynamical properties of a black-box system using a
learning machine. Trained only by a segment of time series of a state variable
recorded at present parameters values, the dynamics of the learning machine at
different training stages can be mapped to the dynamics of the target system
along a particular path in its parameter space, following an appropriate
training strategy that monotonously decreases the cost function. This path is
important, because along that, the primary dynamical properties of the target
system will subsequently emerge, in the simple-to-complex order, matching
closely the evolution law of certain self-evolved systems in nature. Why such a
path can be reproduced is attributed to our training strategy. This particular
function of the learning machine opens up a novel way to probe the global
dynamical properties of a black-box system without artificially establish the
equations of motion, and as such it might have countless applications. As an
example, this method is applied to infer what dynamical stages a variable star
has experienced and how it will evolve in future, by using the light curve
observed presently.Comment: 17 pages, 8 figure
The 2-adic valuations of differences of Stirling numbers of the second kind
Let and be positive integers. Let be the 2-adic
valuation of . By we denote the Stirling numbers of the second
kind. In this paper, we first establish a convolution identity of the Stirling
numbers of the second kind and provide a detailed 2-adic analysis to the
Stirling numbers of the second kind. Consequently, we show that if and is odd, then except
when and , in which case . This solves a
conjecture of Lengyel proposed in 2009.Comment: 20 page
Divisibility by 2 of Stirling numbers of the second kind and their differences
Let and be positive integers and be a nonnegative integer.
Let and be the 2-adic valuation of and the sum of
binary digits of , respectively. Let be the Stirling number of the
second kind. It is shown that where
and . Furthermore, one gets that
, where ,
and . Finally, it is proved that if and is not
a power of 2 minus 1, then
where , if is a power of
2, and otherwise. This confirms a conjecture of Lengyel raised in
2009 except when is a power of 2 minus 1.Comment: 23 pages. To appear in Journal of Number Theor
The universal Kummer congruences
Let be a prime. In this paper, we present a detailed -adic analysis to
factorials and double factorials and their congruences. We give good bounds for
the -adic sizes of the coefficients of the divided universal Bernoulli
number when is divisible by . Using these we
then establish the universal Kummer congruences modulo powers of a prime
for the divided universal Bernoulli numbers when is
divisible by .Comment: 20 pages. To appear in Journal of the Australian Mathematical Societ
The 2-adic valuations of Stirling numbers of the second kind
In this paper, we investigate the 2-adic valuations of the Stirling numbers
of the second kind. We show that if
and only if . This confirms a conjecture of
Amdeberhan, Manna and Moll raised in 2008. We show also that for any positive integer , where is the sum of
binary digits of . It proves another conjecture of Amdeberhan, Manna and
Moll.Comment: 9 pages. To appear in International Journal of Number Theor
Methods of exploring energy diffusion in lattices with finite temperature
We discuss two methods for exploring energy diffusion in lattices with finite
temperature in this paper. The first one is the energy-kick (EK) method. To
apply this method, one adds an external energy kick to a particle in the
lattice, and tracks its evolution by evolving the kicked system. The second one
is the fluctuation-correlation (FC) method. The formula for calculating the
probability density function (PDF) using the canonical ensemble is slightly
revised and extended to the microcanonical ensemble. We show that the FC method
has advantages over the EK method theoretically and technically. Theoretically,
the PDF obtained by the FC method reveals the diffusion processes of the inner
energy while the PDF obtained by the EK method represents that of the kick
energy. The diffusion processes of the inner energy and the external energy
added to the system, i.e., the kick energy, may be different quantitatively and
even qualitatively depending on models. To show these facts, we study not only
the equilibrium systems but also the stationary nonequilibrium systems.
Examples showing that the inner energy and the kick energy may have different
diffusion behavior are reported in both cases. The technical advantage enables
us to study the long-time diffusion processes and thus avoids the finite-time
effect.Comment: 10 pages;7figur
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