AUTOMATED FUEL DESIGN OPTIMIZATION FOR HIGH FLUX ISOTOPE REACTOR LOW ENRICHED URANIUM CORE DESIGN

• Main Authors: Bae J. W., Betzler B. R., Chandler D., Ilas G.
• Format: Article
• Language: English
• Published: EDP Sciences 2021-01-01
• Series: EPJ Web of Conferences
• Subjects: optimization; leu; hfir
• Online Access: https://www.epj-conferences.org/articles/epjconf/pdf/2021/01/epjconf_physor2020_02032.pdf

The low enriched uranium (LEU) conversion project for the High Flux Isotope Reactor (HFIR) requires that the converted core design perform as well as or better than the current high enriched uranium core design with respect to key performance metrics, such as isotope production, while maintaining sufficient safety margins. Various designs and fuel shapes have been explored in previous optimization studies. A suite of scripts has been developed for HFIR LEU design and analysis to simplify the reactor physics and thermal hydraulics (TH) analyses. The scripts include generating a high-fidelity 3D HFIR model to perform core depletion simulations with the SHIFT Monte Carlo code, performing an essential rod criticality search during depletion, parsing SHIFT output to determine HFIR key metrics, and performing TH analysis with the HFIR Steady-State Heat Transfer Code. Previously, these scripts were separated and required human interaction between simulation stages. These scripts have been modernized and integrated into a single Python package (the Python HFIR Analysis and Measurement Engine or PHAME) to streamline execution and avoid potential human error. After modernizing the suite of scripts into a single, automated workflow, the tool set was wrapped into an in-house metaheuristic optimization driver that enables different optimization methods, such as simulated annealing and particle swarm. The optimization driver samples a fuel shape, runs PHAME, calculates the cost function with the metrics returned from PHAME, and repeats those steps until it finds an optimal fuel shape. This work demonstrates the workflow of a comprehensive, automated reactor design study and how metaheuristic optimization methods can be leveraged to fine-tune a design parameter like fuel shape. This workflow of wrapping an optimization driver on a full-scale reactor analysis suite increases design and analysis efficiency.