Development of a New Thermochemistry Solver for Multiphysics Simulations of Nuclear Materials

Nuclear materials are highly complex multiscale, multiphysics systems, and an effective prediction of nuclear reactor performance and safety requires simulation capabilities that tightly couple different physical phenomena. The Idaho National Laboratory’s Multiphysics Object Oriented Simulation Environment (MOOSE) provides the computational foundation for performing such simulations and currently consists of the continuum scale fuel performance code Bison and the mesoscale phase-field code Marmot. With the move towards advanced reactors that employ high temperature fluids compared to conventional reactors, corrosion has become a problem of great interest. A new application called Yellowjacket is under development to directly couple thermodynamic equilibrium and kinetics with phase field models in order to model corrosion in advanced reactors. As part of Yellowjacket, a thermochemistry code is being developed to perform thermochemical equilibrium calculations for a range of different materials, which is currently in its infancy. This paper describes the recent progress towards the development of Yellowjacket and presents the plans for developing capabilities of practical interest to the nuclear industry.

[1]  M. Hillert The compound energy formalism , 2001 .

[2]  Veena Tikare,et al.  Modeling and simulation of nuclear fuel materials , 2010 .

[3]  Srdjan Simunovic,et al.  Numerical verification of equilibrium thermodynamic computations in nuclear fuel performance codes , 2011 .

[4]  Gunnar Eriksson,et al.  A procedure to estimate equilibrium concentrations in multicomponent systems amd related applications , 1989 .

[5]  M.H.A. Piro Updating the estimated assemblage of stable phases in a Gibbs energy minimizer , 2017 .

[6]  Marie-Aline Van Ende,et al.  FactSage thermochemical software and databases, 2010–2016 , 2016 .

[7]  Javier Ortensi,et al.  Physics-based multiscale coupling for full core nuclear reactor simulation , 2014 .

[8]  Gunnar Eriksson,et al.  The modified quasichemical model I—Binary solutions , 2000 .

[9]  Yi Wang,et al.  Computational Thermodynamics of Materials , 2016 .

[10]  Gunnar Eriksson,et al.  The modified quasi-chemical model: Part IV. Two-sublattice quadruplet approximation , 2001 .

[11]  P. E. Raison,et al.  Thermodynamic assessment of the LiF-NiF 2 , NaF-NiF 2 and KF-NiF 2 systems , 2018, The Journal of Chemical Thermodynamics.

[12]  M.H.A. Piro,et al.  Global optimization algorithms to compute thermodynamic equilibria in large complex systems with performance considerations , 2016 .

[13]  Selmer M. Johnson,et al.  Chemical Equilibrium in Complex Mixtures , 1958 .

[14]  Patrice Chartrand,et al.  The modified quasi-chemical model: Part III. Two sublattices , 2001 .

[15]  Patrice Chartrand,et al.  Thermodynamic optimization of the (Na2O + SiO2 + NaF + SiF4) reciprocal system using the Modified Quasichemical Model in the Quadruplet Approximation , 2011 .

[16]  Marius Stan,et al.  Discovery and design of nuclear fuels , 2009 .

[17]  M. Hillert,et al.  Some viewpoints on the use of a computer for calculating phase diagrams , 1981 .

[18]  M.H.A. Piro,et al.  On the interpretation of chemical potentials computed from equilibrium thermodynamic codes: Applications to molten salts , 2019 .

[19]  Markus Hans Alexander Piro Computation of Thermodynamic Equilibria Pertinent to Nuclear Materials in Multi-Physics Codes , 2011 .

[20]  O. Beneš,et al.  Molten Salt Reactor Fuel and Coolant , 2020, Comprehensive Nuclear Materials.

[21]  Gunnar Eriksson,et al.  FactSage thermochemical software and databases , 2002 .