The development of fusion power requires a facility for assessing the behaviour of materials subjected to damage from 14 MeV neutrons with displacement damage levels of up to 150 atomic displacements per atom. The proposed International Fusion Materials Irradiation Facility (IFMIF) will enable experiments to be conducted that closely fit these requirements. When designing experiments it is at first sight natural to suggest a uniformly sampled test matrix over the domain of interest. However, as the irradiation volume in IFMIF will be limited, it is appropriate to consider how the set of experiments may be optimised.
In the present work we suggest a set of experiments designed using predictive Bayesian neural network models created using published data for irradiated reduced-activation martensitic steels. It has been possible to identify gaps in knowledge on the basis of model-perceived uncertainties, and to access trends in order to determine more optimal sampling of experiments.
Journal of Nuclear Materials, Volumes 367-370, 2007, Pages 1586-1589
A short audio interview of Harry Bhadeshia by Mathew Peet, on the content of the paper.
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