Machine learning approach for predicting nuclide composition in nuclear fuel: Bypassing traditional depletion and transport calculations
Year: 2024
Type: article
Abstract: Accurate prediction of nuclide composition evolution during fuel depletion typically requires computationally intensive transport calculations to determine 1-group cross sections for all transmutation reactions in the reaction network. These cross sections are inuenced by the neutron energy spectrum, which varies with both depletion and changing state parameters, such as fuel and moderatortemperatures. This study investigates the feasibility of a machine learning-based model designed to predict nuclide composition directly from power history and state parameters, bypassing the need to calculate group cross sections and solve the Bateman equation. Model testing on an unseen power and state parameter history showed a mean absolute relative error of 0.471% for 235U. Due to its rapid inference speed, this approach is well suited to applications where traditional transport calculations are prohibitively expensive.
Source: Annals of Nuclear Energy
Authors: Yi Meng Chan, Jan Dufek
Institutions: AlbaNova, KTH Royal Institute of Technology
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,690
OpenAlex ID: W4405146189