3,152 research outputs found
A Solvable Model for Nonlinear Mean Field Dynamo
We formulate a solvable model that describes generation and saturation of
mean magnetic field in a dynamo with kinetic helicity, in the limit of large
magnetic Prandtl number. This model is based on the assumption that the
stochastic part of the velocity field is Gaussian and white in time (the
Kazantsev-Kraichnan ensemble), while the regular part describing the back
reaction of the magnetic field is chosen from balancing the viscous and Lorentz
stresses in the MHD Navier-Stokes equation. The model provides an analytical
explanation for previously obtained numerical results.Comment: 6 page
Ferromagnetic Detectors of Axions in RF (S - X) Band
The (pseudo) Goldstone bosons arise naturally in many modern theories such as
supergravity, superstring theory and variants of general relativity with
torsion. By the other hand, there are well known indications that a large part
of the Universe mass exists in a form of dark matter. The most attractive model
of the dark matter is non-relativistic gas of the light elementary particles
weakly interacting with the "usual" matter \cite{b2} - \cite{b4}. We describe
ferromagnetic detectors, for search of arion(axion), where a high-sensitive
two-channel SHF receiver is used. Its sensitivity reaches to ,
with time of accumulation . Fourier analysis of signal provides a
survey in zone up to with spectral resolution .
There was applied a high sensitive SHF receiver based on a special computer
method of coherent accumulation of signals. It is possible to use the receiver
in other precise experiments: measuring of electron/positron beams polarization
in storage rings, investigation of parity violation, investigation of
atmosphere with radars etc.Comment: 6 pages, LaTeX, no figure
Turbulence without pressure in d dimensions
The randomly driven Navier-Stokes equation without pressure in d-dimensional
space is considered as a model of strong turbulence in a compressible fluid. We
derive a closed equation for the velocity-gradient probability density
function. We find the asymptotics of this function for the case of the gradient
velocity field (Burgers turbulence), and provide a numerical solution for the
two-dimensional case. Application of these results to the velocity-difference
probability density function is discussed.Comment: latex, 5 pages, revised and enlarge
Shell to shell energy transfer in MHD, Part I: steady state turbulence
We investigate the transfer of energy from large scales to small scales in
fully developed forced three-dimensional MHD-turbulence by analyzing the
results of direct numerical simulations in the absence of an externally imposed
uniform magnetic field. Our results show that the transfer of kinetic energy
from the large scales to kinetic energy at smaller scales, and the transfer of
magnetic energy from the large scales to magnetic energy at smaller scales, are
local, as is also found in the case of neutral fluids, and in a way that is
compatible with Kolmogorov (1941) theory of turbulence. However, the transfer
of energy from the velocity field to the magnetic field is a highly non-local
process in Fourier space. Energy from the velocity field at large scales can be
transfered directly into small scale magnetic fields without the participation
of intermediate scales. Some implications of our results to MHD turbulence
modeling are also discussed.Comment: Submitted to PR
A note on Burgers' turbulence
In this note the Polyakov equation [Phys. Rev. E {\bf 52} (1995) 6183] for
the velocity-difference PDF, with the exciting force correlation function
is analyzed. Several solvable cases are
considered, which are in a good agreement with available numerical results.
Then it is shown how the method developed by A. Polyakov can be applied to
turbulence with short-scale-correlated forces, a situation considered in models
of self-organized criticality.Comment: 11 pages, Late
Strong magnetohydrodynamic turbulence with cross helicity
Magnetohydrodynamics (MHD) provides the simplest description of magnetic
plasma turbulence in a variety of astrophysical and laboratory systems. MHD
turbulence with nonzero cross helicity is often called imbalanced, as it
implies that the energies of Alfv\'en fluctuations propagating parallel and
anti-parallel the background field are not equal. Recent analytical and
numerical studies have revealed that at every scale, MHD turbulence consists of
regions of positive and negative cross helicity, indicating that such
turbulence is inherently locally imbalanced. In this paper, results from high
resolution numerical simulations of steady-state incompressible MHD turbulence,
with and without cross helicity are presented. It is argued that the inertial
range scaling of the energy spectra (E^+ and E^-) of fluctuations moving in
opposite directions is independent of the amount of cross-helicity. When cross
helicity is nonzero, E^+ and E^- maintain the same scaling, but have differing
amplitudes depending on the amount of cross-helicity.Comment: To appear in Physics of Plasma
Structure Function Scaling in Compressible Super-Alfvenic MHD Turbulence
Supersonic turbulent flows of magnetized gas are believed to play an
important role in the dynamics of star-forming clouds in galaxies.
Understanding statistical properties of such flows is crucial for developing a
theory of star formation. In this letter we propose a unified approach for
obtaining the velocity scaling in compressible and super--Alfv\'{e}nic
turbulence, valid for arbitrary sonic Mach number, \ms. We demonstrate with
numerical simulations that the scaling can be described with the
She--L\'{e}v\^{e}que formalism, where only one parameter, interpreted as the
Hausdorff dimension of the most intense dissipative structures, needs to be
varied as a function of \ms. Our results thus provide a method for obtaining
the velocity scaling in interstellar clouds once their Mach numbers have been
inferred from observations.Comment: published in Physical Review Letter
Using machine learning to speed up new and upgrade detector studies: a calorimeter case
In this paper, we discuss the way advanced machine learning techniques allow
physicists to perform in-depth studies of the realistic operating modes of the
detectors during the stage of their design. Proposed approach can be applied to
both design concept (CDR) and technical design (TDR) phases of future detectors
and existing detectors if upgraded. The machine learning approaches may speed
up the verification of the possible detector configurations and will automate
the entire detector R\&D, which is often accompanied by a large number of
scattered studies. We present the approach of using machine learning for
detector R\&D and its optimisation cycle with an emphasis on the project of the
electromagnetic calorimeter upgrade for the LHCb detector\cite{lhcls3}. The
spatial reconstruction and time of arrival properties for the electromagnetic
calorimeter were demonstrated.Comment: Talk presented on CHEP 2019 conferenc
Test beam studies of the TRD prototype filled with different gas mixtures based on Xe, Kr, and Ar
Towards the end of LHC Run1, gas leaks were observed in some parts of the
Transition Radiation Tracker (TRT) of ATLAS. Due to these leaks, primary Xenon
based gas mixture was replaced with Argon based mixture in various parts.
Test-beam studies with a dedicated Transition Radiation Detector (TRD)
prototype were carried out in 2015 in order to understand transition radiation
performance with mixtures based on Argon and Krypton. We present and discuss
the results of these test-beam studies with different active gas compositions.Comment: 5 pages,12 figures, The 2nd International Conference on Particle
Physics and Astrophysics (ICPPA-2016); Acknowledgments section correcte
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