Development of a molten salt thermal conductivity model and database for advanced energy systems. (15th May 2023)

• Record Type: Journal Article
• Authors: Yang, Huiqiang; Gallagher, Ryan; Chartrand, Patrice; Gheribi, Aïmen E.
• Is Part Of: Solar energy. Volume 256(2023)
• Page Start: 158
• Page End: 178
• Publication Date: 2023-05-15
• Subjects: Molten salts -- Thermal conductivity review -- Thermal energy storage materials -- Kinetic theory -- Temperature dependent thermal conductivity database; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2023.04.009
• Languages: English
• ISSNs: 0038-092X

Abstract

Abstract: A theoretical model for predicting the temperature-dependent thermal conductivity of pure molten salts, both simple and complex, is presented. The model is based on kinetic theory and incorporates Einstein's concept of minimum thermal conductivity. The proposed formulation can consider the magnitude of thermal conductivity for separate salts, including complex and polymerizing salts. The model's thermal conductivity predictions were compared to reliable experimental data in the literature and a previously recommended thermal conductivity model using the Bland–Altman method. The comparison showed accurate thermal conductivity predictions relative to the reliable experimental data, with an average deviation of 10% or less. The model's predictions were also compared to the experimental data on an individual basis for halide, divalent halide, carbonate, nitrate, nitrite, sulfate, and hydroxide molten salts, demonstrating reliable predictions for the molten salts studied and improved accuracy over the previous model. Lastly, a database of simple and complex molten salts, with the necessary parameters for modeling their thermal conductivity, is recommended. Highlights: A database for the thermal conductivity of more than 60 molten salts is presented. The model is developed based on the kinetic theory. Few key thermophysical properties are required for the model parameterization. The predictive capability of the model is found to be very good. The model is valid from the melting temperature up to 75% of the boiling temperature.