A predictive approach for the compositional and temperature representation of thermal conductivity in multicomponent molten salt systems for advanced energy applications

Abstract

This study presents a theoretical model, based on kinetic theory, for accurately predicting thermal conductivity in molten salt mixtures, a vital factor in designing advanced energy systems such as concentrating solar power, thermal energy storage, and advanced nuclear reactors. Applicable to non-complex, non-reciprocal systems, the model represents thermal conductivity with respect to composition and temperature. We evaluated the model using molecular dynamics simulations on binary systems from fluoride, chloride, bromide, iodide salt families. The model predictions were also compared against existing experimental datasets for those families as well as binary nitrates and carbonates. The model demonstrated an improvement in predictive accuracy over the ideal linear estimation and showed reasonable agreement with the reliable experimental data. Deviations from ideal conductivity, analyzed using the Redlich-Kister method, pointed to the molecular weight and thermal conductivity of individual compounds as primary influencing factors. When applied to predict the thermal conductivity of higher order eutectic salt mixtures relevant to energy applications, the model showed predictive accuracy comparable to empirical methods. The model was further applied to generate compositional mapping to quantify deviations from ideality over the compositional space. This advance enhances new molten salt mixtures evaluation efficiency and extends the model to complex mixtures.