YALINA-booster subcritical assembly pulsed-neutron experiments : data processing and spatial corrections.

[摘要] The YALINA-Booster experiments and analyses are part of the collaboration between Argonne National Laboratory of USA and the Joint Institute for Power & Nuclear Research - SOSNY of Belarus for studying the physics of accelerator driven systems for nuclear energy applications using low enriched uranium. The YALINA-Booster subcritical assembly is utilized for studying the kinetics of accelerator driven systems with its highly intensive D-T or D-D pulsed neutron source. In particular, the pulsed neutron methods are used to determine the reactivity of the subcritical system. This report examines the pulsed-neutron experiments performed in the YALINA-Booster facility with different configurations for the subcritical assembly. The 1141 configuration with 90% U-235 fuel and the 1185 configuration with 36% or 21% U-235 fuel are examined. The Sjoestrand area-ratio method is utilized to determine the reactivities of the different configurations. The linear regression method is applied to obtain the prompt neutron decay constants from the pulsed-neutron experimental data. The reactivity values obtained from the experimental data are shown to be dependent on the detector locations inside the subcritical assembly and the types of detector used for the measurements. In this report, Bell's spatial correction factors are calculated based on a Monte Carlo model to remove the detector dependences. The large differences between the reactivity values given by the detectors in the fast neutron zone of the YALINA-Booster are reduced after applying the spatial corrections. In addition, the estimated reactivity values after the spatial corrections are much less spatially dependent.

[发布日期] 2010-10-11

[学科分类] 核物理和高能物理

[关键词] ACCELERATORS;ANL;DATA PROCESSING;SLIGHTLY ENRICHED URANIUM;FAST NEUTRONS;KINETICS;NEUTRON SOURCES;NUCLEAR ENERGY;PROMPT NEUTRONS;SUBCRITICAL ASSEMBLIES;BELARUS;INTERNATIONAL COOPERATION;REACTIVITY;CORRECTIONS