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In this paper we present a set of image analysis techniques used for extraction of information from cross-sectional images of nuclear fuel assemblies, achieved from gamma emission tomography measurements. These techniques are based on template matching, an established method for identifying objects with known properties in images. We demonstrate a rod template matching algorithm for identification and counting of the fuel rods present in the image. This technique may be applicable in nuclear safeguards inspections, because of the potential of verifying the presence of all fuel rods, or potentially discovering any that are missing. We also demonstrate the accurate determination of the position of a fuel assembly, or parts of the assembly, within the imaged area. Accurate knowledge of the assembly position enables detailed modelling of the gamma transport through the fuel, which in turn is needed to make tomographic reconstructions quantifying the activity in each fuel rod with high precision. Using the full gamma energy spectrum, details about the location of different gamma-emitting isotopes within the fuel assembly can be extracted. We also demonstrate the capability to determine the position of supporting parts of the nuclear fuel assembly through their attenuating effect on the gamma rays emitted from the fuel. Altogether this enhances the capabilities of non-destructive nuclear fuel characterization. [ABSTRACT FROM AUTHOR]

The nuclear industry uses fuel performance codes to demonstrate integrity preservation of fuel rods. These codes include a complex system of models with empirical constants that one needs to calibrate for best estimates and uncertainties. However, deriving the appropriate level of uncertainty is often challenging due to model inadequacies. This paper presents a method to address model inadequacies by adapting the mean and covariance of the model parameters so that the propagated uncertainty conforms with the spread of the residuals rather than calibrating the model parameters directly. We demonstrate the method on synthetic data sets from an artificial test-bed containing a cladding oxidation and a hydrogen pick-up model. A repeated validation using many synthetic data sets shows that the method is robust and handles model inadequacies appropriately in most cases. Furthermore, we compare with traditional calibration and show model inadequacy leads to underestimation of uncertainties if not addressed. • A simplified model for hydrogen pick-up and oxidation is implemented. • Synthetic data with the desired level of model inadequacy is generated. • Model inadequacy harms calibration, leading to underestimated uncertainties. • An inflated uncertainty of model parameters can account for model inadequacy. • Repeated calibration shows a promising method for treating model inadequacies. [ABSTRACT FROM AUTHOR]