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Title
[Graduate Seminar] 9th Apr(Fri) Prof. Takuji Oda(Seoul National Univ.) "Computational simulations for impurity chemistry
Date
2021.06.10
Writer
기계공학부
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▣ Title: Computational simulations for impurity chemistry in liquid metals

Speaker: Takuji Oda(Professor)

Affiliation: Seoul National University

▣ Date: 2021. 4. 9.(Fri) 13:00

▣ Venue: Online(Zoom)

▣ Host: Prof. Keonwook Kang


▣ Abstract

Liquid metals have been widely studied for various applications in the field of nuclear engineering. For example, liquid Pb and Pb-Bi eutectic (LBE) are used as coolants in lead-cooled fast reactors (LFRs), liquid Na as coolant in sodium fast reactors (SFRs), and liquid Li and Li-Pb eutectic as tritium breeders in fusion reactors. The use of liquid metals in nuclear reactors requires a thorough understanding of the behavior of impurities in liquid metals, which is relevant to the integrity of structural materials as well as the radiation safety with respect to the transport of radioactive impurity in the event of an accident. This presentation introduces the application of atomistic simulations, namely, first-principles calculation and molecular dynamics (MD) calculation, in the study of impurity chemistry in liquid metals. First, the solution enthalpy of impurity in liquid Na, which is one of the fundamental data for impurity chemistry but is still missing or unreliable for many impurity elements, is calculated. It is shown that one can determine solution enthalpy within around 30 kJ/mol, which is comparable with the accuracy of experiment, if the calculation is done properly.

Second, for large-scale simulation, classical MD simulation is used. By reusing the first-principles calculation data used in the solution enthalpy calculation, an accurate potential model can be effectively generated and complex phenomena such as sodium burning can be reasonably simulated. Finally, we discuss future research directions toward a systematic understanding of impurity chemistry and the discovery of new materials through atomistic simulations aided by machine learning



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