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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 k  eff   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.

MAPPER – A NOVEL CAPABILITY TO SUPPORT NUCLEAR MODEL VALIDATION AND MAPPING OF BIASES AND UNCERTAINTIES