Abstract
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 set of multi-dimensional published data with doses <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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
