In the MST device, a magnetized plasma is confined by a magnetic field in a configuration called a reversed field pinch (RFP). The plasma exhibits magnetic and other fluctuations that are believed to be responsible for the rapid escape of the plasma across the magnetic field to the walls where it is cooled. This anomolous transport is a major impediment to the development of magnetic fusion energy. Since the dynamics producing the fluctuations are deterministic, the fluctuations are presumably chaotic with a high dimension. In fact, Watts placed a lower limit of about 15 on the correlation dimension of the observed magnetic turbulence in MST. If chaotic fluctuations are responsible for the anomolous transport, it would be of great benefit to control their characteristics. On way this might be done is to apply a sinusoidal perturbation of some carefully chosen frequency to some accessible system parameter. In the most optimistic case, this perturbation would suppress the chaos and reduce the dynamics to a simple limit cycle with a concurrent substantial reduction in plasma transport.

On way to study such prospects is to construct an artificial neural
network model of the magnetic dynamics of MST and to add periodic perturbations
to the model in a numerical simulation. The neural net chosen has
*N* neurons, with the input of each neuron consisting of the superposition
of the weighted sum of the output of all the neurons (including itself).
The neurons are represented by hyperbolic tangent squashing functions that
provide the nonlinearity necessary for chaos:

The parameter *s* is a scale factor on the weights and is initially
chosen as *s* = 1. The *N* x *N* matrix of weights
*W _{ij}* is adjusted by a variant of simulated annealing so
that the

In the data, the dominant feature is a rotating resistive tearing mode with a frequency of about 14 kHz.

After training the net for several days of 200 MHz CPU time, the output
of the network on the same scale as above has the waveform shown below:

The simulation does capture some sense of the rotating mode seen in
the experiment, with a frequency of about 11 kHz. It appears that
the signal from the 26th coil from the top might be accidentally inverted,
and this blemish is captured by the neural net. The model is not
chaotic, however, as evidenced by calculation of the largest Lyapunov exponent,
which is indistinguishable from zero, and the solution appears to be a
limit cycle in this 64-dimensional space. To obtain a chaotic solution,
the parameter *s* was increased to 1.2, giving the result below:

This case has a largest Lyapunov exponent (base-*e*) of 0.028/iteration.
The rotating oscillation persists at a frequency of about 10 kHz.

To this case was added a perturbation *s* = *s*_{0}
+ *s*_{1} sin (2p*ft*)] with
*s*_{0} = 1.2, and the perturbation amplitude was scanned
over the range 0 < *s*_{1} < *s*_{0} and
frequency over the range 1 kHz < *f* < 100 kHz, and the largest
Lyapunov exponent was calculated for each combination of *s*_{1}
and *f*. The result was plotted with a gray scale in which the
darkest color (black) indicates an exponent of zero (chaos completely suppressed)
and the lightest color (white) indicates an enhancement of the chaos
(doubling of the Lyapunov exponent):

The general result is a decrease in chaos for increasing perturbation
amplitude, with certain frequencies such as 13 kHz being especially effective.
The striations in the upper left are caused by truncating the calculation
at a time (500 iterations) that is not an integer number of perturbation
cycles. Since the Lyapunov exponent generally increases with *s*,
chaos is enhanced when the final value of *s* is positive and diminished
when it is negative.

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