Tag: multiphysics

MocDown

Jeffrey Seifried (alumnus)

MocDown (http://jeffseif.github.io/MocDown/) is an efficient tool which loosely couples simulations for neutron transport, isotopic transmutation, thermo-fluids, and the equilibrium core composition search within advanced nuclear reactor cores. The development of MocDown focused on facilitating both fast runtime (by employing concurrent threading and efficient regex parsing when possible) and fast post-processing (with simple and consistent hierarchical storage of result files). MocDown also employs object-oriented programming in Python 3 for flexible modification with external libraries.

To do so, MocDown couples three models for self-consistent simulations: thermo-fluids, neutron transport, and transmutation and recycling. The MocDown accelerated recycling scheme efficiently finds the equilibrium cycle, whose isotopic composition matches that of its successor. Using these techniques, MocDown has been successfully used to simulate the RBWR-Th design, a fuel-self-sustaining nuclear reactor core design which operates with only thorium as its charge.

Adjoint-based uncertainty quantification in multiphysics reactor modeling

Manuele Aufiero, Michael Martin, Massimiliano Fratoni

Coupled neutronics-thermal/hydraulics simulations are of great interest for the analysis and design of nuclear reactors. Ongoing studies of advanced and GEN-IV reactors call for the adoption of accurate modeling tools that are based on Monte Carlo neutron transport and CFD-based T/H solutions. In this framework, the capability to propagate uncertainties in the input data through the coupled simulation is highly desirable.

Recently, Generalized Perturbation Theory (GPT) methods have been implemented in continuous energy Monte Carlo codes, broadly expanding their capabilities. Some of these methods (e.g., available in the Serpent code) are suitable to be adopted in combination with Open Source finite-volume libraries for continuum mechanics solvers (e.g., the OpenFOAM C++ multiphysics toolkit).

The present project involves the projection of the input uncertainties and the reactor generalized responses onto sets of orthogonal basis functions, along with the adoption of extended GPT methods for the calculation of sensitivities in the coupled problems. The comparison of nuclear data uncertainty propagation results against standard methods in simple benchmark cases shows that the new approach might provide a reliable and efficient option for Uncertainty Quantification in multiphysics problems.

Multi-physics modeling of fluoride-cooled high-temperature reactors (FHRs)

Xin Wang, Dan Shen, Katy Huff, Manuele Aufiero, Massimiliano Fratoni, April Novak

Multi-physics modeling of fluoride-cooled high-temperature reactors (FHRs)To improve understanding of coupled physics in FHRs, this work involves the development of tools and methods for coupling at thermal hydraulics and neutronics within the context of FHRs. Low-dimensional models relying on simplified neutron kinetics and heat transfer have been implemented in a python package, PyRK. Higher dimensional models that couple these physics in finite element frameworks (including both MOOSE and COMSOL) are also being developed. Finally, models which coupled monte carlo simulation with CFD tools are also being iterated upon.