The mathematical functions necessary for Rietveld refinement of time-of-flight neutron powder diffraction patterns from spallation sources are developed and a computer program for least-squares analysis is described. The results of Rietveld refinements of nickel and a low-carbon steel are described and discussed. The method fully exploits the high resolution (Ad/d ,--0"3---0.5%) available with powder diffractometers currently in operation on these sources and examples are given of precise determination of atom coordinates, thermal parameters, lattice parameters and the detection of small strains.

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An on-line predictor of the time to disruption has been installed on the ASDEX Upgrade tokamak. It is suitable either for avoidance of disruptions or for mitigation of those that are unavoidable. The prediction uses a neural network trained on eight plasma parameters and their time derivatives extracted from 99 disruptive discharges. The network was tested off-line over 500 discharges and was found to work reliably and to be able to predict the majority of the disruptions. The trained network was installed on-line, tested over 128 discharges and used to inject killer pellets to mitigate the disruption loads.

First results are reported on the prediction of disruptions in one tokamak, based on neural networks trained on another tokamak. The studies use data from the JET and ASDEX Upgrade devices, with a neural network trained on just 7 normalised plasma parameters. In this way, a simple single layer perceptron network trained solely on JET correctly anticipated 67% of disruptions on ASDEX Upgrade in advance of 0.01 seconds before the disruption. The converse test led to a 69% success rate in advance of 0.04 seconds before the disruption in JET. Only one overall time scaling parameter is allowed between the devices, which can be introduced from theoretical arguments. Disruption prediction performance based on such networks trained and tested on the same device shows even higher success rates (JET: 86%; ASDEX Upgrade 90%), despite the small number of inputs used and simplicity of the network. It is found that while performance for networks trained and tested on the same device can be improved with more complex networks and many adjustable weights, for cross machine testing the best approach is a simple single layer perceptron. This offers the basis of a potentially useful technique for large future devices such as ITER, which with further development, might help to reduce disruption frequency and minimise the need for a large disruption campaign to train disruption avoidance systems.

Tokamak Energy Ltd, UK, is developing spherical tokamaks using high temperature superconductor magnets as a possible route to fusion power using relatively small devices. We present an overview of the development programme including details of the enabling technologies, the key modelling methods and results, and the remaining challenges on the path to compact fusion.

Neutron and X-ray small-angle scattering provide, along with electron microscopy and diffraction, the principal techniques for the microscopic characterization of materials. Neutron, X-ray and electron beams each have quite different properties. In fact, each has unique advantages. The penetration of neutrons through most materials is responsible for many applications. The ever-increasing intensity of available X-ray beams is opening new fields. The advantage of electron beams is their ability to work in both real and reciprocal space. The problems of transforming the results of an experiment in reciprocal space to give an interpretation in real space are central to small-angle scattering, and are discussed. Several examples will be given of the successful use of small-angle neutron scattering applied to problems where other techniques have failed to make a decisive contribution.The transmission electron microscope is also readily switched to its diffraction mode to give crystallographic information from a localized area of the sample. The imaging mode may often be used to locate a given feature of the microstructure, for example a single precipitate particle. The microscope can then be switched to diffraction mode to reveal the atomic structure and orientation of this particular ~ particle.It will be seen that the small-angle diffraction techniques, both neutron (Gerald & Kostorz, 1978;Kostorz, 1987) and X-ray (Glatter & Kratky, 1982), cover the microstructural range perfectly. However, they give us different information, as we consider later.

Theoretically, the normalized plasma pressure (β) at which a neoclassical tearing mode (NTM) is triggered is expected to depend on normalized Larmor radius (ρ*) and normalized collisionality (ν), and this has formed the basis for the way in which NTM onset scalings are quoted for many devices. However, new analyses of JET data show that such ρ*–ν based scalings are non-predictive, with discharges largely following such scalings over the majority of their duration while in H-mode. Neural network techniques indicate that a key additional parameter to include is the sawtooth period, providing a better degree of predictability. Indeed, this parameter appears more important than ρ* in predicting NTM onset. Analysis using cases where sawteeth are modified by localized heating and current drive indicates that it is the sawtooth that governs where it is along the NTM onset scaling trajectory that the NTM is triggered, rather than leading to departures from the scaling. Finally, exploring data from cross-device similarity experiments shows similar absolute values in NTM onset βN across devices of differing size and ρ* range. This suggests the possibility that a simple ρ*-based extrapolation for ITER may be inappropriate and that NTM threshold levels may be more directly related to the absolute value of β, suggesting a higher β threshold for ITER, at least if large sawteeth are avoided.