Prediction of yield stress in highly irradiated ferritic steels

Abstract

The design of any fusion power plant requires information on the irradiation hardening of low-activation ferritic/martensitic steels beyond the range of most present measurements. Neural networks have been used by Kemp et al (J. Nucl. Mater. 348 311–28) to model the yield stress of some 1811 irradiated alloys. The same dataset has been used in this study, but has been divided into a training set containing the majority of the dataset with low irradiation levels, and a test set which contains just those alloys which have been irradiated above a given level. For example some 4.5% of the alloys were irradiated above 30 displacements per atom. For this ‘prediction’ problem it is found that simpler networks with fewer inputs are advantageous. By using target-driven dimensionality reduction, linear combinations of the atomic inputs reduce the test residual below that achievable by adding inputs from single atoms. It is postulated that these combinations represent ‘mechanisms’ for the prediction of irradiated yield stress.