Abstract
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.
Highlights
Designing tokamak devices such as ITER and DEMO requires in-depth knowledge and understanding of material damage, transmutation and activation due to 14.1 MeV neutrons, arising from Deuterium-Tritium (D-T) reactions
Whilst plans are underway to develop a DEMO Oriented Neutron Source (DONES) [2] to tackle this issue, very little data is available for material exposure under 14 MeV neutron irradiation
We examine the use of supervised learning techniques using Artificial Neutral Networks (ANNs) for peak classification [19][20] for comparison
Summary
Designing tokamak devices such as ITER and DEMO requires in-depth knowledge and understanding of material damage, transmutation and activation due to 14.1 MeV neutrons, arising from Deuterium-Tritium (D-T) reactions These reactors expect to experience neutron fluxes of the order of 1018 neutrons m−2s−1, and quantities such as nuclear heating, neutron damage and transmutation-induced radioactivity are crucial for their design and operation [1]. Spectral data is included in the nuclear data libraries and FISPACT-II is capable of reading spectral lines and can appropriately produce discrete spectra based on radioisotopes present in the inventory This offers a direct approach to compare and validate existing nuclear data libraries with experimental data in the fusion energy regime [4] [5]. This work presents an automated approach to perform direct comparison and validation between ASP gamma spectra data and inventory simulations for a wide range of materials and reactions
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