Manuel Acevedo Ruiz

Two-Bar Model for the Dynamics and Stability of Electrodynamic Tethers

Previously a new dynamic instability that affected electrodynamic tethers in inclined orbits was studied, with a simple one-bar model that neglected the contribution of the tether lateral dynamics. The e exibility of the tether (lateral dynamics ), however, plays an important role in the overall motion of the system as shown by numerical simulations of a bare-tether generator in a circular inclined orbit. The same analytical techniques of the previous work arenow applied to investigate the dynamics and stability of an electrodynamic tether system modeled by two articulatedbarsthat accountforthelowestlateralmodesof thetether. Theanalysis,whichcanbedirectly extended to any electrodynamic tether system, has been focused on two particular, but important cases: the combination of a conductiveand a nonconductive leadertether (as in the Propulsive Small Expendable Deployment System )and a homogeneous, conductive tether. The lateral dynamics is extremely rich, with skip rope motion, instability peaks, and chains ofbifurcationsfor differentregions ofthe parameter space. The sameenergy pumping mechanism that destabilizes the rigid model (one bar) is found to drive an even faster instability of the lateral modes. Damping, which has not been included in the analysis, could change this unstable behavior.

On the minimum orbital intersection distance computation: a new effective method

The computation of the Minimum Orbital Intersection Distance (MOID) is an old, but increasingly relevant problem. Fast and precise methods for MOID computation are needed to select potentially hazardous asteroids from a large catalogue. The same applies to debris with respect to spacecraft. An iterative method that strictly meets these two premises is presented.

Review of disruption predictors in nuclear fusion: Classical, from scratch and anomaly detection approaches

Disruption predictors are implemented as data-driven models obtained from machine learning techniques. These data-driven models are deduced from a training process with thousands of discharges (both disruptive and non-disruptive). ITER or DEMO, the next step devices cannot afford to wait for hundreds of disruptions to start predicting. A novelty approach for disruption prediction is to avoid the use of past discharges for learning purposes. The objective is to learn in every discharge how a safe plasma evolution is and to trigger an alarm when anomalies in the data flow appear. Of course, these anomalies have to be signatures of the phenomenons precursors. By applying these ideas in JET to a dataset of more than 1700 non-disruptive shots and more than 550 that ended in a disruption, the success rate is about 90% and the false alarm rate is slightly above 7%. On average, the alarm is triggered with an anticipation time above 200 ms.