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The demand for clean energy production and storage has increased interest in molten salt technologies, including Molten Salt Reactors (MSR). Understanding of how molten salts properties change with respect to temperature and structure is vital to establishing efficient, cost effective MSR systems. Research into these materials however has been limited due to the difficulty in accurately measuring properties of these reactive materials at elevated temperatures and controlled environment in a time efficient way. Much research has turned to molecular dynamic (MD) modeling to alleviate these issues. This research presents a custom fabricated falling ball viscometer system for measuring molten salt viscosity quickly. A model for correlating velocity to viscosity for Re < 300 was also developed for use with this system. The viscometer is demonstrated on eutectic FLiNaK and NaF-ZrF4 (53–47 mol%) up to 150 K above the respective melting points. The results are compared to MD simulations to verify their effectiveness for predicting viscosity and previously reported measurements.
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High-Throughput Prediction of Molten Salt Mixture Density with Supervised Machine Learning
For the development and optimization of molten salt reactors, nuclear fuel cycles, and energy storage materials, the temperature-dependent molten salt properties must be known over a wide range of possible compositions. Yet, for several relevant chloride and fluoride salt systems, significant gaps in data still exist, which inhibit the development of key advanced energy technologies. [1] Filling all of these gaps with high-temperature experiments is inherently challenging, especially due to the corrosive, volatile, and hazardous nature of these salts. Meanwhile, carefully validated atomistic simulations (ab initio, classical or machine learning-based) are capable of predicting thermophysical properties but are highly computationally expensive [2-5], limiting our ability to screen over large temperature-compositional spaces. In this work, we propose to circumvent these limitations by using supervised machine learning (ML) models to learn from existing bulk density data and predict densities of new and unseen mixtures.
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- Award ID(s):
- 2138456
- PAR ID:
- 10500911
- Publisher / Repository:
- American Nuclear Society
- Date Published:
- Journal Name:
- Transactions of the American Nuclear Society
- Volume:
- 129
- Issue:
- 1
- Page Range / eLocation ID:
- 482-483
- Subject(s) / Keyword(s):
- machine learning, molten salt mixtures, density prediction
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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