EPJ Web Conf.
Volume 146, 2017ND 2016: International Conference on Nuclear Data for Science and Technology
|Number of page(s)||4|
|Section||Nuclear Data for Applications|
|Published online||13 September 2017|
Experiments at the GELINA facility for the validation of the self-indication neutron resonance densitometry technique
1 Society and Policy Support, Belgian nuclear research centre SCK·CEN, Mol, Belgium
2 Ecole polytechnique de Bruxelles – Université libre de Bruxelles ULB, Brussels, Belgium
3 European Commission, Joint Research Centre JRC, Directorate G, Geel, Belgium
a e-mail: firstname.lastname@example.org
Published online: 13 September 2017
Self-Indication Neutron Resonance Densitometry (SINRD) is a passive non-destructive method that is being investigated to quantify the 239Pu content in a spent fuel assembly. The technique relies on the energy dependence of total cross sections for neutron induced reaction. The cross sections show resonance structures that can be used to quantify the presence of materials in objects, e.g. the total cross-section of 239Pu shows a strong resonance close to 0.3 eV. This resonance will cause a reduction of the number of neutrons emitted from spent fuel when 239Pu is present. Hence such a reduction can be used to quantify the amount of 239Pu present in the fuel. A neutron detector with a high sensitivity to neutrons in this energy region is used to enhance the sensitivity to 239Pu. This principle is similar to self-indication cross section measurements. An appropriate detector can be realized by surrounding a 239Pu-loaded fission chamber with appropriate neutron absorbing material. In this contribution experiments performed at the GELINA time-of-flight facility of the JRC at Geel (Belgium) to validate the simulations are discussed. The results confirm that the strongest sensitivity to the target material was achieved with the self-indication technique, highlighting the importance of using a 239Pu fission chamber for the SINRD measurements.
© The Authors, published by EDP Sciences, 2017
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