We developed a new nuclear data adjustment method for experimental data containing outliers. This method mitigates the effect of outliers by applying M-estimation, a type of robust estimation, to the conventional nuclear data adjustment method using sensitivity coefficients. Based on the M-estimation, we derived a weighted nuclear data adjustment formula and developed a weight calculation method. The weighted nuclear data adjustment formula was derived by weighting the function to take the extremum of the conventional nuclear data adjustment. The weighting of each nuclear characteristic is calculated from the difference between the measured and calculated values of the nuclear characteristic. This weight calculation method can evaluate the validity of each nuclear characteristic by considering correlations between nuclear characteristics using singular value decomposition. The proposed method and the conventional method were compared and verified by twin experiments. In the twin experiments, the nuclear data were adjusted using experimental data that intentionally included outliers. As a result of twin experiments, it was confirmed that the nuclear data were adjusted robustly and appropriately even with the experimental data containing outliers.
In the design of a fast breeder reactor (FBR) core for the light water reactor (LWR) to FBR transition stage, it is indispensable to grasp the effect of a wide range of fuel composition variations on the core characteristics. This study finds good correlations between burnup reactivity and safety parameters, such as the sodium void reactivity and Doppler coefficient, for various fuel compositions and determines the mechanisms behind these correlations with the aid of sensitivity analyses. It is clarified that the Doppler coefficient is actually correlated with the other core characteristics by considering the constraint imposed by the requirement of sustaining criticality on the fuel composition variations. These correlations make it easy to specify the various properties ranges for core reactivity control and core safety, which are important for core design in determining the core specifications and performance. They provide significant information for FBR core design for the transition stage. Moreover, as an application of the above-mentioned correlations, a simplified burnup reactivity index is developed for rapid and rational estimation of the core characteristic variations. With the use of this index and these correlations, the core characteristic variations can be estimated for various fuel compositions without repeating the core calculations.
The applicability of Akaike’s Bayesian Information Criterion (ABIC) to the covariance modeling in the cross-section adjustment method has been investigated. In the conventional cross-section adjustment method, the covariance matrices are assumed to be true. However, this assumption is not always appropriate. To improve the reliability of the cross-section adjustment method, the estimation of the covariance model using the metric ABIC has been introduced, and the performance of ABIC has been investigated through simple numerical experiments. This paper derives the formula to efficiently evaluate ABIC which is represented by a lower rank matrix to enable numerical experiments with large samples in a realistic computation time. From the results of the numerical experiments, it has been confirmed that ABIC tends to select a covariance model with fewer hyperparameters and a smaller variance for the estimation error. However, it has also been found that this desirable property of ABIC will be lost when the structure of the covariance model is far from the true one.
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