Integrity monitoring method for dry storage casks using artificial neural network

Abstract

As the storage terms of spent nuclear fuel (SNF) are extended, monitoring of safety related parameters becomes more important for safe operation of the dry storage casks. The cask integrity assurance will be greatly enhanced if the canister pressure and the peak cladding temperature (PCT) can be reliably inferred from canister surface temperatures (CSTs), and this would also eliminate need for any measurement sensor that penetrates the canister pressure boundary. In the present work, a method to reliably predict the pressure and the PCT using artificial neural network (ANN) models has been developed using CSTs measured at axially spaced positions. To validate the method, a test rig with a single fuel assembly was constructed, and pressure variation tests were carried out. ANN models were developed using the experimental data and the models were augmented by the functional link with CST profile shape information for improved prediction performance. A computational fluid dynamics (CFD) code was then used to simulate these pressure variation tests, and comparison was made to assess the discrepancies between the simulation and the measurement results. The ANN models were trained on the databases built from the CFD calculation results. The measured CST signals were converted to the CSTs of simulation hyperspace on which the ANN models were trained. The conversion was done with the compensation factors obtained at a known reference state. The ANN monitoring models showed good prediction performances in spite of the discrepancies between the measurement and the simulation results.