Automatic Procedure for Thermal NDE of Delaminations in CFRP by Using Neural Networks

This work is a first step in detecting and characterizing defects in an automatic way by using artificial intelligence. Transient thermal NDE by IR thermography is the method used for such a purpose. Data are processed by Neural Networks

An active feedback recovery technique from disruption events induced by m=2 n=1 tearing modes in ohmically heated tokamak plasmas

We present experimental results of magnetic feedback control on the m=2, n=1 tearing mode in RFX-mod operated as a circular ohmically heated tokamak. The feedback suppression of the non-resonant m=2, n=1 Resistive Wall Mode (RWM) in q(a)<2 plasmas is a well-established result of RFX-mod. The control of the tearing counterpart, which develops in q(a)>2 equilibrium, is instead a more difficult issue. In fact, the disruption induced by a growing amplitude m=2, n=1 tearing mode can be prevented by feedback only when the resonant surface q=2 is close to the plasma edge, namely 2<q(a)<2.5, and the electron density does not exceed approximately half of the Greenwald limit. A combined technique of tearing mode and q(a) control has been therefore developed to recover the discharge from the most critical conditions: the potentially disruptive tearing mode is converted into the relatively benign RWM by suddenly decreasing q(a) below 2. The experiments demonstrate the concept with 100% of successful cases. The q(a) control has been performed through the plasma current, given the capability of the toroidal loop-voltage power supply of RFX-mod. We also propose a path for controlling q(a) by acting on the plasma shape, which could be applied to medium size elongated tokamaks