Neural network analysis of Charpy transition temperature of irradiated low-activation martensitic steels

G.A. Cottrell, R. Kemp, H.K.D.H. Bhadeshia, G.R. Odette and T. Yamamoto


We have constructed a Bayesian neural network model that predicts the change, due to neutron irradiation, of the Charpy ductile-brittle transition temperature (ΔDBTT) of low activation martensitic steels given a 40-dimensional set of data from the literature. The irradiation doses were less than 100 displacements per atom (dpa). Results show the high significance of irradiation temperature and (dpa)1/2 in determining ΔDBTT. Sparse data regions were identified by the size of the modelling uncertainties, indicating areas where further experimental data are needed. The method has promise for selecting and ranking experiments on future irradiation materials test facilities.

Journal of Nuclear Materials, Volumes 367-370, 2007, Pages 603-609

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