Artificial Neural Network based Particle Swarm Optimization solution approach for the inverse depletion of used nuclear fuel

Abstract

This work builds upon previous efforts to apply inverse analysis in the determination of the initial conditions and burnup history of used nuclear fuel for forensic purposes. In this work, the inverse depletion problem is defined and solved using a surrogate-based approach equipped with the Particle Swarm Optimization algorithm and an Artificial Neural Network surrogate model. The proposed approach is outlined and verified via a series of mockup forensic case studies based upon a VVER-1000 assembly model depleted via SCALE6.1 KENO VI module.The case studies considered retrieving the fuel initial enrichment and burnup using the final used nuclear fuel isotopic content. Results indicate that the proposed approach can estimate the initial fuel enrichment and maximum burnup with reasonable computational cost and acceptable accuracy. The relative error in estimating the fuel initial enrichment was <6% while that of the actual fuel burnup was <3% for all mockup case studies.