Abstract
The declared linear density of 238U and 235U in fresh low enriched uranium light water reactor fuel assemblies can be verified for nuclear safeguards purposes using a neutron coincidence counter collar in passive and active mode, respectively. The active mode calibration of the Uranium Neutron Collar - Light water reactor fuel (UNCL) instrument is normally performed using a non-linear fitting technique. The fitting technique relates the measured neutron coincidence rate (the predictor) to the linear density of 235U (the response) in order to estimate model parameters of the nonlinear Padé equation, which traditionally is used to model the calibration data. Alternatively, following a simple data transformation, the fitting can also be performed using standard linear fitting methods. This paper compares performance of the nonlinear technique to the linear technique, using a range of possible error variance magnitudes in the measured neutron coincidence rate. We develop the required formalism and then apply the traditional (nonlinear) and alternative approaches (linear) to the same experimental and corresponding simulated representative datasets. We find that, in this context, because of the magnitude of the errors in the predictor, it is preferable not to transform to a linear model, and it is preferable not to adjust for the errors in the predictor when inferring the model parameters.
Original language | English |
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Pages (from-to) | 70-75 |
Number of pages | 6 |
Journal | Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment |
Volume | 811 |
DOIs | |
State | Published - Mar 1 2016 |
Funding
The work described in this paper was funded by the U.S. DOE under action sheet AS-16 between The National Nuclear Energy Commission of Brazil (CNEN) and the United States Department of Energy ( DOE ).
Funders | Funder number |
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U.S. Department of Energy | |
Comissão Nacional de Energia Nuclear |
Keywords
- Errors in predictor
- Fresh LWR fuel assay
- Linear regression
- Neutron collar