Browsing by Author "Pavez, Cristian"

Pulsed plasma discharges, such as the plasma focus, are a source of pulsed X rays, therefore it is desirable to understand the relationship between this fast transient phenomena and the electrical variables of the discharge. Parameters from the electrical diagnostic signals are typically used to characterize the plasma focus discharge and for the correlations with X rays measurements via scatter plots. To further evaluate relevant information in the electrical signals, besides the characteristic parameters, an implementation of different types of machine learning algorithms, that included deep learning, was performed. A classification of pulses associated with an X rays measurement, in terms of the electrical signals data as input, was carried out. Two approaches were compared: the selection of the characteristic parameters and the use of the entire signals so the algorithms could find additional information for the classification task. The electrical diagnostic signals corresponded to: the voltage at the electrodes of the discharge chamber measured with a resistive voltage divider; time variation of the circuit current measured with a Rogowski coil and an inductive loop sensor; and the electromagnetic burst from the circuit measured with a Vivaldi antenna. The X rays measurement corresponded to the signal obtained from a scintillator-photomultiplier. In terms of the performance of the algorithms models in this classification problem, the results indicated that there is no significative improvements when using the entire signal or the selection of characteristic parameters. The best results were obtained when the following parameters were used: voltage at time of gas breakdown, voltage at time of pinch, current at time of pinch, time derivative of current at time of pinch, time from breakdown to pinch, and the Fast Fourier Transform of the part of the Vivaldi antenna signal related to the pinch event.

A remote and non-invasive diagnostic of the plasma focus using antennas is presented in this work. The main motivation is the application of such diagnostic in a miniaturized plasma accelerator, based on the plasma focus architecture, as a cube satellite thruster. The evaluation of this proposal was carried out measuring a hundred of joules plasma focus operation simultaneously with the inductive measurement and antennas. Three different antennas tuned in the ultra high frequency range were tested: a monopole, Vivaldi and helical. The high frequency transients detected with the antennas were time correlated to the known inductive measurement features. The initial dielectric breakdown and later plasma pinch and subsequent disruption (i.e. the source of the propulsion) were identified to be the principal phenomena to be detected. Signal parameter correlations between the inductive sensor and the antennas showed that the pinch phenomena can be correlated with the antenna signals. Good correlation results were obtained with the monopole antenna when using peak value and signal energy parameter from the antenna transient. An improvement in the correlation results, for the helical and Vivaldi antennas, was obtained when calculating the frequency band energy. In this case, the Vivaldi antenna achieved the best results. The results of the monopole antenna make it an alternative remote sensor for plasma focus, but for the application of a miniaturized plasma focus as pulsed plasma thruster, the Vivaldi antenna is a more feasible design to replace the inductive diagnostic due to its compact design in comparison to the monopole.