Publications

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Non-equilibrium sorption of contaminants in ground water systems is examined from the perspective of sorption rate estimation. A previously developed Markov transition probability model for solute transport is used in conjunction with a new conditional probability-based model of the sorption and desorption rates based on breakthrough curve data. Two models for prediction of spatially varying sorption and desorption rates along a one-dimensional streamline are developed. These models are a Markov model that utilizes conditional probabilities to determine the rates and an ensemble Kalman filter (EnKF) applied to the conditional probability method. Both approaches rely on a previously developed Markov-model of mass transfer, and both models assimilate the observed concentration data into the rate estimation at each observation time. Initial values of the rates are perturbed from the true values to form ensembles of rates and the ability of both estimation approaches to recover the true rates is examined over three different sets of perturbations. The models accurately estimate the rates when the mean of the perturbations are zero, the unbiased case. For the cases containing some bias, addition of the ensemble Kalman filter is shown to improve accuracy of the rate estimation by as much as an order of magnitude.

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Proliferation resistance features that reduce the likelihood of diversion of nuclear materials from the civilian nuclear power fuel cycle are critical for a global nuclear future. A framework that monitors process information continuously can demonstrate the ability to resist proliferation by measuring and reducing diversion risk, thus ensuring the legitimate use of the nuclear fuel cycle. The automation of new nuclear facilities requiring minimal manual operation makes this possible by generating instantaneous system state data that can be used to track and measure the status of the process and material at any given time. Sandia National Laboratories (SNL) and the Japan Atomic Energy Agency (JAEA) are working in cooperation to develop an advanced transparency framework capable of assessing diversion risk in support of overall plant transparency. The ''diversion risk'' quantifies the probability and consequence of a host nation diverting nuclear materials from a civilian fuel cycle facility. This document introduces the details of the diversion risk quantification approach to be demonstrated in the fuel handling training model of the MONJU Fast Reactor.

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This work focuses on different methods to generate confidence regions for nonlinear parameter identification problems. Three methods for confidence region estimation are considered: a linear approximation method, an F-test method, and a Log-Likelihood method. Each of these methods are applied to three case studies. One case study is a problem with synthetic data, and the other two case studies identify hydraulic parameters in groundwater flow problems based on experimental well-test results. The confidence regions for each case study are analyzed and compared. Although the F-test and Log-Likelihood methods result in similar regions, there are differences between these regions and the regions generated by the linear approximation method for nonlinear problems. The differing results, capabilities, and drawbacks of all three methods are discussed.

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