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Liquid metals are employed as a coolant in liquid metal fast reactors (LMFR) and are considered breeders and coolants for future power-producing fusion reactors. The interaction of liquid metals with other coolants, such as air and water, is one of the possible occurrences in liquid metal-cooled reactors. Although the SIMMER-III computer code is currently in the validation and verification phase, it is a prospective candidate code for correctly investigating possible accidents. This study aims to determine the stability and well-posedness of the SIMMER-III code Eulerian-Eulerian two-fluid model (TFM) under any such accident scenario. This work also considers the influence of the virtual mass force and diffusion forces in momentum on TFM stability in accelerated liquid metal, steam, and non-condensable gaseous flows throughout all flow regimes. The characteristics method is used to assess the ill-posed nature of TFM for all types of accidents and accident scenarios in a hypothetical simplified system model with several components (Lead–Lithium, non-condensable gases, and water vapor). It has been discovered that the analysis findings vary from the air and water two-component two-phase flows (characteristics roots spectrum and error growth rate patterns) and are particularly sensitive to the diffusion and virtual mass coefficients. This is because liquid metals have a higher density than liquid water and steam, resulting in strong virtual mass forces and weak diffusion forces in liquid-metal and gas two-phase flows. Because of the extensive range of possible interactions between fluxes of different components, producing an accurate representation of diffusion in multi-component mixtures is difficult. Because of this, it is strongly suggested that the virtual mass coefficient and diffusion coefficients be handled more accurately for these kinds of flows. The values of these coefficients significantly affect how accurate proposed TFM predictions are, but there hasn't been much research on how to estimate them. The study also sheds light on the model's accuracy and highlights the areas where the model's predictions will be mathematically trustworthy.
Saraswat et al. (Thu,) studied this question.