Modeling and benchmarking XRF analysis using MCNP for applications in accident tolerant fuel and cladding

Abstract

There is an interest in using nondestructive testing (NDT) methods for the preliminary investigation of accident-tolerant fuel cladding materials, such as chromium (Cr) coated Zircaloy-4 (Zr4). One promising application is X-ray fluorescence (XRF) analysis. Computational methods, such as Monte Carlo N-Particle Transport (MCNP) 6.2, can be used to expand algorithms based on XRF measurements, however, it has been demonstrated that MCNP is more sensitive to modeling imperfections at lower energies ($\le$ 80 keV). In this work, several MCNP models were developed to evaluate the XRF measurements given by a Niton XL-5 device to minimize deviations at low energies. The final model was benchmarked to an experimental XRF measurement of Cr-coated Zr4 taken by the XL-5. The percent error in the resulting XRF peak intensities was within $\pm$ 4.92% for the K_α1 peaks and within $\pm$ 16.0% for the K_β1. The discrepancies in the magnitude of these errors are largely due to the K_β1 peaks having far fewer counts in the spectra that were compared. Nonetheless, these results demonstrate the potential for MCNP 6.2 to accurately predict low-energy X-ray interactions such as XRF. The deviations observed were similar to those seen in the 0.1–1 MeV range in prior works, despite only being in the $\sim$ 5–30 keV range themselves.