A team of researchers at Idaho State University is working in partnership with the General Electric Vernova Advanced Research Center (ARC) on the Resonance Absorption Densitometry for Materials Assay Security Safeguards (RADMASS) project. RADMASS is funded under the Optimizing Nuclear Waste and Advanced Reactor Disposal Systems initiative through the U.S. Department of Energy’s Advanced Research Projects Agency. The research program, led by ARC, is developing methods to enhance the commercial viability of fast reactor used nuclear fuel recycling using pyroprocessing with a novel input accountancy method.

The RADMASS research includes the analysis of a nondestructive assay (NDA) method, called dual isotope notch observer (DINO) detection, that is a nuclear resonance fluorescence technology. Modeling performed with MCNP is guiding the design and optimization of the DINO detection system, with assistance from a machine learning model. Shielding analysis is also being performed in MCNP to inform the pyroprocessing plant design and NDA equipment placement while maximizing the signal-to-noise ratio for the DINO detection system.

A parametric study of fuel types and burnups, used as inputs to a MATLAB/Simulink pyroprocessing safeguards model, explores the impacts on the standard error of inventory difference, a key metric in safeguards compliance. The modeled data are for training a machine learning model for optimization of pyroprocessing while meeting safeguards requirements. A figure of merit is under development using the same modeled data, in addition to input accountancy uncertainty values, throughput, recovery rates, and more, to assist in the optimization needed for commercial viability while maintaining safeguards compliance.