Neutron Tomography of Spent Fuel Casks

Dry casks for spent nuclear fuel (SNF) ensure the safe storage of SNF and provide radiation shielding. However, presence thick encompassing several layers steel concrete makes inspection a challenging task. Fast neutron interrogation is viable method nondestructive assay dry casks. In this study, we performed Monte Carlo simulation-based study associated with machine-learning-based image reconstruction to verify content We studied use transmission back-scattered measurements assess potential damage assemblies or pin diversion during transportation used Geant4 model realistic HI-STAR 100 cask, MPC-68 canister basket, GE-14 assembly irradiated by D-T generator. Several bundle scenarios were simulated. The angular distribution neutrons scattered cask was identify diversions inside cask. A at least 75% its pins removed can be identified drop in signature larger than $$2\sigma$$ compared fully loaded scenario. combined an iterative algorithm convolutional neural network (CNN) obtain cross-sectional proposed imaging approach allows locating position missing when performing tomographic overall scan time less two hours, using commercial generator source strength $$10^{10}$$ n/s 4 $$\pi$$ solid angle.