Inverse Solutions in Spectroscopic Analysis with Applications to Problems in Global Safeguards

Year
2011
Author(s)
Alexander A. Solodov - Oak Ridge National Laboratory
M. Ehinger - Oak Ridge National Lab
Vladimir Protopopescu - Oak Ridge National Laboratory
Charles F. Weber - Oak Ridge National Laboratory
Catherine E. Romano - Oak Ridge National Laboratory
Abstract
This work describes a nondestructive strategy and algorithm designed to evaluate the burnup and plutonium content of light-water-reactor spent fuel and thereby confirm declared values. In contrast with previous methods that focus on only a few photopeaks (e.g., 137 Cs at 662 keV), the present approach involves the entire gamma spectrum up to 2000 keV. Spectra are used as input for the inverse code INDEPTH, which is designed to predict reactor parameters (fuel enrichment, power level, irradiation time, and cooling time) when given either a set of nuclide inventories or the gamma spectrum that they produce. This approach has the advantage of often making possible the determination of parameters other than burnup when they are unknown or in doubt. In addition, error in one photopeak evaluation is mitigated by the inclusion of the entire spectrum. The solution procedure involves multiple runs of the forward code ORIGEN/ARP, each of which produces an extensive list of nuclides formed through depletion/decay processes. The gamma spectrum of these nuclides is compared with the gamma spectrum from a detector through a bin-by-bin sum of squared error. New choices for the reactor parameters that are input to ORIGEN/ARP are determined using a gradient search technique, and the best parameter set is that which minimizes the squared error between calculated and measured gamma spectra. The method is applied to the analysis of gamma data taken from various sections of actual spent reactor fuel and is compared with declared values and other methods of evaluation. The sensitivity of the inverse solution with respect to various parameters is calculated and indicates that the algorithm is stable and robust. One example includes the presence of multiple solutions, each of which can be characterized using additional information