Technology and development of self-reinforced polymer composites

In recent years there has been an increasing amount of interest, both commercially and scientifically, in the emerging field of "self-reinforced polymer composites". These materials, which are sometimes also referred to as "single polymer composites", or "all-polymer composites", were first conceived in the 1970s, and are now beginning to appear in a range of commercial products. While high mechanical performance polymer fibres or tapes are an obvious precursor for composite development, various different technologies have been developed to consolidate these into two- or three-dimensional structures. This paper presents a review of the various processing techniques that have been reported in the literature for the manufacture of self-reinforced polymer composites from fibres or tapes of different polymers, and so exploit the fibre or tape performance in a commercial material or product

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures