Abstract: The analytical solutions for geodesic acoustic
eigenmodes in tokamak plasmas with circular concentric magnetic
surfaces are found. In the frame of ideal magnetohydrodynamics the
dispersion relation taking into account the toroidal coupling between
electrostatic perturbations and electromagnetic perturbations with
poloidal mode number |m| = 2 is derived. In the absence of such
a coupling the dispersion relation gives the standard continuous
spectrum of geodesic acoustic modes. The analysis of the existence
of global eigenmodes for plasma equilibria with both off-axis
and on-axis maximum of the local geodesic acoustic frequency is
performed.
Abstract: Semiconductor detector arrays are widely used in
high-temperature plasma diagnostics. They have a fast response,
which allows observation of many processes and instabilities in
tokamaks. In this paper, there are reviewed several diagnostics based
on semiconductor arrays as cameras, AXUV photodiodes (referred
often as fast “bolometers") and detectors of both soft X-rays and
visible light installed on the COMPASS tokamak recently. Fresh
results from both spring and summer campaigns in 2012 are
introduced. Examples of the utilization of the detectors are shown on
the plasma shape determination, fast calculation of the radiation
center, two-dimensional plasma radiation tomography in different
spectral ranges, observation of impurity inflow, and also on
investigation of MHD activity in the COMPASS tokamak discharges.
Abstract: This paper is mainly concerned with the application of
a novel technique of data interpretation for classifying measurements
of plasma columns in Tokamak reactors for nuclear fusion
applications. The proposed method exploits several concepts derived
from soft computing theory. In particular, Artificial Neural Networks
and Multi-Class Support Vector Machines have been exploited to
classify magnetic variables useful to determine shape and position of
the plasma with a reduced computational complexity. The proposed
technique is used to analyze simulated databases of plasma equilibria
based on ITER geometry configuration. As well as demonstrating the
successful recovery of scalar equilibrium parameters, we show that
the technique can yield practical advantages compared with earlier
methods.
Abstract: This paper is mainly concerned with the application of a novel technique of data interpretation to the characterization and classification of measurements of plasma columns in Tokamak reactors for nuclear fusion applications. The proposed method exploits several concepts derived from soft computing theory. In particular, Artifical Neural Networks have been exploited to classify magnetic variables useful to determine shape and position of the plasma with a reduced computational complexity. The proposed technique is used to analyze simulated databases of plasma equilibria based on ITER geometry configuration. As well as demonstrating the successful recovery of scalar equilibrium parameters, we show that the technique can yield practical advantages compares with earlier methods.