Integrated Nondestructive Assay Systems to Estimate Plutonium in Spent Fuel Assemblies

Abstract An integrated nondestructive assay (NDA) system combining active (neutron generator) and passive neutron detection and passive gamma (PG) detection is being analyzed in order to estimate the amount of plutonium, verify initial enrichment, burnup, and cooling time, and detect partial defects in a spent fuel assembly (SFA). Active signals are measured using the differential die-away (DDA), delayed neutron (DN), and delayed gamma (DG) techniques. Passive signals are measured using total neutron (TN) counts and both gross and spectral resolved gamma counts. To quantify how a system of several NDA techniques is expected to perform, all of the relevant NDA techniques listed above were simulated as a function of various reactor conditions such as initial enrichment, burnup, cooling time, assembly shuffling pattern, reactor operating conditions (including temperature, pressure, and the presence of burnable poisons) by simulating the NDA response for five sets of light water reactor assemblies. This paper compares the performance of several exploratory model-fitting options (including neural networks, adaptive regression with splines, iterative bias reduction smoothing, projection pursuit regression, and regression with quadratic terms and interaction terms) to relate data simulated with measurement and model error effects from various subsets of the NDA techniques to the total Pu mass. Isotope masses for SFAs and expected detector responses (DRs) for several NDA techniques are simulated using MCNP, and the DRs become inputs to the fitting process. Such responses include eight signals from DDA, one from DN, one from TN, and up to seven from PG; the DG signal will be examined separately. Results are summarized using the root-mean-squared estimation error for plutonium mass in held-out subsets of the data for a range of model and measurement error variances. Different simulation assumptions lead to different spent fuel libraries relating DRs to Pu mass. Some results for training with one library and testing with another library are also given.

[1]  Jianming Ye.  On Measuring and Correcting the Effects of Data Mining and Model Selection , 1998 .

[2]  N. Ensslin,et al.  Passive Nondestructive Assay of Nuclear Materials , 1991 .

[3]  T. Burr,et al.  Uncertainty Quantification for New Approaches to Spent Fuel Assay , 2012 .

[4]  Sophie Grape,et al.  Update on the Next Generation Safeguards Initiative’s Spent Fuel Nondestructive Assay Project , 2013 .

[5]  Tom L Burr,et al.  Biased Regression: The Case for Cautious Application , 2005, Technometrics.

[6]  D. Ruppert,et al.  Measurement Error in Nonlinear Models , 1995 .

[7]  Chris Chatfield,et al.  Statistics for Technology-A Course in Applied Statistics. , 1984 .

[8]  Aristides Gionis,et al.  Assessing data mining results via swap randomization , 2007, TKDD.

[9]  Robert B. Gramacy,et al.  Ja n 20 08 Bayesian Treed Gaussian Process Models with an Application to Computer Modeling , 2009 .

[10]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[11]  Nicolas W. Hengartner,et al.  Iterative bias reduction: a comparative study , 2013, Stat. Comput..

[13]  Alan W. Hunt,et al.  Delayed Gamma-Ray Spectroscopy for Spent Nuclear Fuel Assay , 2012 .

[14]  H. Chipman,et al.  BART: Bayesian Additive Regression Trees , 2008, 0806.3286.

[15]  J. G. Richard,et al.  Determination of the plutonium content in a spent fuel assembly by passive and active interrogation using a differential die-away instrument , 2013 .

[16]  Z. Reitermanová.  Data Splitting , 2010 .

[17]  Knepper,et al.  A study of the effect of measurement error in predictor variables in nondestructive assay , 2000, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.