Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2584165

Multiphysics Running-In Simulations for Pebble-Bed Reactors with Griffin

Hanophy, Joshua Thomas [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000342091011)·Balestra, Paolo [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000219837201)·Wang, Yaqi [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000237373498)·Ortensi, Javier [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000316853916)·Schunert, Sebastian [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000275564818)
View original source ↗Read source document ↗Export metadata ↓

Abstract

Griffin, a Multiphysics Object-Oriented Simulation Environment (MOOSE)–based application targeting transient modeling of advanced reactors, has been used recently to model pebble-bed reactors (PBRs). The modeling effort has focused thus far on equilibrium core calculations. A new capability to simulate the running-in phase of PBR operation has been added to Griffin. This work demonstrates the new capability with a coupled multiphysics running-in simulation. Griffin computes power densities in the core at each time step of the running-in simulation and passes these to Pronghorn, which models fluid flow and heat transfer to calculate pebble surface temperatures. These surface temperatures are used along with the power densities in a heat conduction model to compute average fuel and moderator temperatures, which are passed back to Griffin and accounted for with temperature-dependent cross sections. This work also describes a novel methodology for determining appropriate pebble feed rates and control rod positioning during the running-in simulation. Furthermore, the RZ-geometry model used in this work requires minimal computational resources and can be used for optimization and uncertainty studies in future works.

Explore related subjects

GriffinPebble Bed Reactorburning-inrunning-instartup

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hanophy, Joshua Thomas [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000342091011), Balestra, Paolo [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000219837201), Wang, Yaqi [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000237373498), Ortensi, Javier [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000316853916), Schunert, Sebastian [Idaho National Laboratory (INL), Idaho Falls, ID (United States)] (ORCID:0000000275564818). 2025-06-02. Multiphysics Running-In Simulations for Pebble-Bed Reactors with Griffin. https://doi.org/10.1080/00295639.2025.2497025

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2025

Deterministic High-Fidelity Neutronics Simulation of Pebble Bed Reactors Using Pebble Tracking Transport

The pebble tracking transport (PTT) algorithm offers a high-fidelity deterministic approach for neutron transport for pebble bed reactors (PBRs). This approach requires the mesh for the active-core region to consist exclusively of tetrahedral elements, where each node in the pebble-packing region represents a pebble centroid. This paper investigates the application of PTT for full-scale PBRs, considering both the isothermal and the temperature-dependent core conditions. Macroscopic cross sections are generated using Serpent 2 full-core eigenvalue simulations where pebbles are grouped into disjoint subsets using machine learning. To minimize the need for individual cross-section sets for each pebble in the core, K-means clustering is used to group pebbles by temperature and neutronic environment parameters. Here, we compare the multiplication factor and power rate distributions between PTT simulations using the Griffin reactor physics software and reference solutions from Serpent 2. Our analysis shows that a full-core, high-fidelity PTT calculation produces accurate results with minimal local (pebblewise) errors. Additionally, timing results indicate that PTT simulations converge rapidly on modern supercomputing platforms.

Griffin↗