1,789 research outputs found
Numerical Studies of Weakly Stochastic Magnetic Reconnection
We study the effects of turbulence on magnetic reconnection using
three-dimensional numerical simulations. This is the first attempt to test a
model of fast magnetic reconnection proposed by Lazarian & Vishniac (1999),
which assumes the presence of weak, small-scale magnetic field structure near
the current sheet. This affects the rate of reconnection by reducing the
transverse scale for reconnection flows and by allowing many independent flux
reconnection events to occur simultaneously. We performed a number of
simulations to test the dependencies of the reconnection speed, defined as the
ratio of the inflow velocity to the Alfven speed, on the turbulence power, the
injection scale and resistivity. Our results show that turbulence significantly
affects the topology of magnetic field near the diffusion region and increases
the thickness of the outflow region. We confirm the predictions of the Lazarian
& Vishniac model. In particular, we report the growth of the reconnection speed
proportional to ~ V^2, where V is the amplitude of velocity at the injection
scale. It depends on the injection scale l as ~ (l/L)^(2/3), where L is the
size of the system, which is somewhat faster but still roughly consistent with
the theoretical expectations. We also show that for 3D reconnection the Ohmic
resistivity is important in the local reconnection events only, and the global
reconnection rate in the presence of turbulence does not depend on it.Comment: 8 pages, 8 figure
The role of pressure anisotropy in the turbulent intracluster medium
In low-density plasma environments, such as the intracluster medium (ICM),
the Larmour frequency is much larger than the ion-ion collision frequency. In
such a case, the thermal pressure becomes anisotropic with respect to the
magnetic field orientation and the evolution of the turbulent gas is more
correctly described by a kinetic approach. A possible description of these
collisionless scenarios is given by the so-called kinetic magnetohydrodynamic
(KMHD) formalism, in which particles freely stream along the field lines, while
moving with the field lines in the perpendicular direction. In this way a
fluid-like behavior in the perpendicular plane is restored. In this work, we
study fast growing magnetic fluctuations in the smallest scales which operate
in the collisionless plasma that fills the ICM. In particular, we focus on the
impact of a particular evolution of the pressure anisotropy and its
implications for the turbulent dynamics of observables under the conditions
prevailing in the ICM. We present results from numerical simulations and
compare the results which those obtained using an MHD formalism.Comment: 7 pages, 14 figures, Journal of Physics: Conference Serie
Reconnection Studies Under Different Types of Turbulence Driving
We study a model of fast magnetic reconnection in the presence of weak
turbulence proposed by Lazarian and Vishniac (1999) using three-dimensional
direct numerical simulations. The model has been already successfully tested in
Kowal et al. (2009) confirming the dependencies of the reconnection speed
on the turbulence injection power and the injection scale
expressed by a constraint
and no observed dependency on Ohmic resistivity. In Kowal et al. (2009), in
order to drive turbulence, we injected velocity fluctuations in Fourier space
with frequencies concentrated around , as described in
Alvelius (1999). In this paper we extend our previous studies by comparing fast
magnetic reconnection under different mechanisms of turbulence injection by
introducing a new way of turbulence driving. The new method injects velocity or
magnetic eddies with a specified amplitude and scale in random locations
directly in real space. We provide exact relations between the eddy parameters
and turbulent power and injection scale. We performed simulations with new
forcing in order to study turbulent power and injection scale dependencies. The
results show no discrepancy between models with two different methods of
turbulence driving exposing the same scalings in both cases. This is in
agreement with the Lazarian and Vishniac (1999) predictions. In addition, we
performed a series of models with varying viscosity . Although Lazarian
and Vishniac (1999) do not provide any prediction for this dependence, we
report a weak relation between the reconnection speed with viscosity,
.Comment: 19 pages, 9 figures. arXiv admin note: text overlap with
arXiv:0903.205
Mechanical System Reliability and Cost Integration Using a Sequential Linear Approximation Method
The development of new products is dependent on product designs that incorporate high levels of reliability along with a design that meets predetermined levels of system cost. Additional constraints on the product include explicit and implicit performance requirements. Existing reliability and cost prediction methods result in no direct linkage between variables affecting these two dominant product attributes. A methodology to integrate reliability and cost estimates using a sequential linear approximation method is proposed. The sequential linear approximation method utilizes probability of failure sensitivities determined from probabilistic reliability methods as well a manufacturing cost sensitivities. The application of the sequential linear approximation method to a mechanical system is demonstrated
Mechanical system reliability for long life space systems
The creation of a compendium of mechanical limit states was undertaken in order to provide a reference base for the application of first-order reliability methods to mechanical systems in the context of the development of a system level design methodology. The compendium was conceived as a reference source specific to the problem of developing the noted design methodology, and not an exhaustive or exclusive compilation of mechanical limit states. The compendium is not intended to be a handbook of mechanical limit states for general use. The compendium provides a diverse set of limit-state relationships for use in demonstrating the application of probabilistic reliability methods to mechanical systems. The compendium is to be used in the reliability analysis of moderately complex mechanical systems
Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification
This paper explores the problem of breast tissue classification of microscopy
images. Based on the predominant cancer type the goal is to classify images
into four categories of normal, benign, in situ carcinoma, and invasive
carcinoma. Given a suitable training dataset, we utilize deep learning
techniques to address the classification problem. Due to the large size of each
image in the training dataset, we propose a patch-based technique which
consists of two consecutive convolutional neural networks. The first
"patch-wise" network acts as an auto-encoder that extracts the most salient
features of image patches while the second "image-wise" network performs
classification of the whole image. The first network is pre-trained and aimed
at extracting local information while the second network obtains global
information of an input image. We trained the networks using the ICIAR 2018
grand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method
yields 95 % accuracy on the validation set compared to previously reported 77 %
accuracy rates in the literature. Our code is publicly available at
https://github.com/ImagingLab/ICIAR2018Comment: 10 pages, 5 figures, ICIAR 2018 conferenc
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