An important area of research required for fusion reactor design is the study of materials under high energy neutron irradiation. Deuterium-Tritium (D-T) reactions release 14.1 MeV neutrons and material studies of such high energy neutrons focusing on transmutation and activation are paramount for fusion tokamak devices such as ITER and DEMO. In order to understand neutron damage and transmutation-induced radioactivity in fusion regime energies, a series of experimental campaigns were performed at the ASP facility based at Aldermaston in the UK, which uses a deuteron accelerator to bombard a tritiumloaded target and generate 14 MeV-neutron emission rates of up to 2.5 × 1011 s−1. In this work, a holistic treatment of the 11,000 gamma spectra (time series data) collected over five experimental campaigns is applied to identify radioisotopes and validate nuclear data and the inventory code, FISPACT-II. Whilst previous analysis has examined single spectra and foil irradiation’s using traditional, human-driven methods, this work applies novel methods using Artificial Neural Networks (ANN) and classification algorithms to allow a fully automated approach. Using such methods we show good broad agreement with FISPACT-II inventory simulations, and an overview of results are given as C/E values.
Фрагментация молекулы глютамина электронным ударом
