MAPPER - A novel capability to support nuclear model validation and mapping of biases and uncertainties

This paper overviews the initial results of a new project at the Oak Ridge National Laboratory, supported via an internal seed funding program, to develop a novel computational capability for model validation: MAPPER. MAPPER will eliminate the need for empirical criteria such as the similarity indices often employed to identify applicable experiments for given application conditions. To achieve this, MAPPER uses an information-theoretic approach based on the Kullback-Leibler (KL) divergence principle to combine responses of available or planned experiments with application responses of interest. This is accomplished with a training set of samples generated using randomized experiment execution and application of high-fidelity analysis models. These samples are condensed using reduced order modeling techniques in the form of a joint probability distribution function (PDF) connecting each application response of interest with a new effective experimental response. MAPPER's initial objective will be to support confirmation of criticality safety analysis of storage facilities which require known keff biases for safe operation. This paper reports some of the initial results obtained with MAPPER as applied to a set of critical experiments for which existing similarity-based methods have been shown to provide inaccurate estimates of the biases.