Feasibility Of Fast Neutron Imaging Of Spent Nuclear Fuel Dry Storage Casks

Feasibility of Fast Neutron Imaging of Spent Nuclear Fuel Dry Storage CasksDry storage casks are increasingly used as a viable interim storage of spent nuclear fuel (SNF) bundles at U. S. nuclear reactors. Verifying the condition of the SNF inside sealed canisters or dry casks without reopening the containers is an open technological challenge. Therefore, a reliable method to non-destructively assay SNF casks and verify their content and integrity is needed. SNF bundles are removed from the water pool and loaded into a canister before being transferred inside the interim dry storage cask or transfer cask. This process is performed when SNF bundles are transferred to a reprocessing plant or long-term storage site. During these transfer stages, the SNF could be contained only in the canister and hence be available for a nondestructive assay (NDA). We performed a Monte Carlo simulation-based study to verify the content of SNF dry storage casks using an associate-particle DT neutron generator (APNG). We studied the use of neutron transmission and backscattering measurements to assess the potential damage to fuel assemblies or fuel pin diversion during transportation of dry casks. We used Geant4 to model a realistic HI-STAR 100 cask, MPC-68 canister and basket, and GE-14 fuel assembly irradiated by a DT neutron generator. Several bundle diversion scenarios were simulated. We found that the distribution of the back-scattered neutrons is a signature of the locations of the missing bundles. We developed a linear forward model to approximate the signal response of the missing fuel rods in a tomographic inspection and obtained reconstructed images using an iterative algorithm. We found that the iterative algorithm works well to image the two outermost layers of the SNF bundles inside the canister while is not able to correctly locate missing bundles in the middle of the canister. Therefore, we applied machine learning algorithms to improve the image quality of the inner bundles.