A critical review of eutectic salt property prediction for latent heat energy storage systems. (April 2017)

• Record Type: Journal Article
• Authors: Raud, Ralf; Jacob, Rhys; Bruno, Frank; Will, Geoffrey; Steinberg, Theodore A.
• Is Part Of: Renewable & sustainable energy reviews. Volume 70(2017)
• Page Start: 936
• Page End: 944
• Publication Date: 2017-04
• Subjects: PCM selection -- Eutectic salt PCM -- Phase change material -- Latentheat thermal energy storage -- Enthalpy of fusion estimation -- Latent heatprediction -- Eutectic composition prediction -- Density of eutectic salts -- Thermal conductivity of eutectic salts; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2016.11.274
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: According to the SunShot initiative, one sixth of the levelized cost of electricity for Concentrated Solar Thermal Power is thermal energy storage. For this power generation paradigm to be successful, the cost of every sub-system must be dramatically reduced. However, the search space for possible storage mediums is too large for a brute force experimental search to be feasible. Thus, a more refined approach is necessary. In this paper, eutectic salt combinations are considered as storage medium. The state of the selection process for these eutectics is discussed. Various methods to predict the important thermophysical properties are reported and applied to eutectics whose physical properties are known. Based on single salt properties, the density of molten salt eutectics can be predicted, around their melting point, to within 5%. Prediction of the melting point and composition is accurate to within 7%. However, the estimation of latent heat for multi-component eutectics is not always accurate, and requires more work. Finally, the thermal conductivity of multi-component eutectics has not been well studied; further research is required to corroborate the predictions.