Calculating thermochemical equilibrium for multiphysics simulations of nuclear materials : development of yellowjacket gibbs energy minimiser
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Nuclear fuels and structural materials are highly complex systems that are remarkably challenging to understand and model. Material behaviours are influenced by multiple physical phenomena such as mechanics, chemistry, heat and mass transport, etc. Moreover, lower scale phenomena inform and drive the phenomena at larger scales. The strong interactions between multiple physics at different length and time scales creates a need for multi-scale, multi-physics modelling tools. In nuclear fuels and structural materials, the problem gets compounded by the fact that, in addition to an extreme environment, the composition of the system changes with time. For such complex systems, computational thermodynamics plays a valuable role in predicting many phenomena and is often necessary for understanding and informing others. For this reason, there has been an increasing interest in incorporating equilibrium thermodynamics calculations in multi-physics frameworks such as the Multiphysics Object Oriented Simulation Environment (MOOSE). To simulate corrosion in molten salt reactors, a new MOOSE-based tool named Yellowjacket has been developed and this work contributes to it. The objective of this work is to develop a new equilibrium thermodynamic solver to provide thermodynamic material properties and boundary conditions for Yellowjacket and other MOOSE-based codes. While several thermodynamics codes already exist, the new software, called Yellowjacket–GEM, adds native equilibrium thermodynamic capability to MOOSE and aims to address several concerns such as computational performance, limitations on system size and models, and Software Quality Assurance (SQA). Yellowjacket–GEM exploits the fundamental laws of thermodynamics to solve a non-linear, non-convex optimisation problem. Several thermodynamic models, including the Modified Quasichemical Model in Quadruplet Approximation (MQMQA) were implemented, and state-of-the art numerical solvers in Portable, Extensible Toolkit for Scientific Computation (PETSc) were used to efficiently solve the optimisation problem. In doing so, the work contributes to the understanding of MQMQA which until recently wasn’t well comprehended. Ensuring that the solver gives a true equilibrium solution also requires solving a global optimisation problem without severely compromising performance and reliability. Several global optimisation methods were compared through numerical experiments to objectively select the best approach for implementation. The C++ code follows MOOSE coding standards and SQA procedures and enables direct coupling of thermodynamic equilibrium calculations in multiphysics simulations performed using MOOSE.