Neutron cross-section measurements for validation of nuclear data for fusion

In nuclear fusion power research there is a need to develop materials which can withstand the high levels of neutron flux in order to develop commercially viable power plant systems. It is essential to accurately understand nuclear reactions in materials that are induced by the high energy neutrons produced in the D-T fusion reaction. The quantification of reaction cross sections into a nuclear library, combined with an inventory code such as FISPACT, enable the capability to simulate important neutron irradiation effects associated with fusion materials, such as activation dose rates and gas production.
In order for trust to be placed in the results of simulations it is necessary that the underlying nuclear data is validated. A significant number of experimental cross section measurements performed in the past were targeted at materials and neutron energies relevant to fission power. There is less experimental cross section data in the neutron energy range experienced in fusion devices. Indeed a large component of the most extensive data libraries available in fusion, are largely composed of model-derived data without any supporting experimental data.

CCFE and York have a program of work to perform fusion relevant neutron cross section measurements using UK based neutron facilities. This project will focus on providing experimental data for reactions and materials related to fusion power, with a focus on difficult to measure cross sections related to tritium breeding technologies and associated diagnostics, such as field spectrometry and flux measurements. The tritium breeding system and associated technologies will be an essential part of future fusion power reactors. Improved knowledge of related reactions will be essential to the design of these systems. The project will involve a mixture of simulation work to identify suitable, measurable reactions; experimental work at a UK 14 MeV neutron facility; and subsequent data analysis and comparison with model derived data.