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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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NERS publishes report on machine learning and microreactors
The University of Michigan’s Department of Nuclear Engineering and Radiological Sciences (NERS) has published a summary of a study on nuclear microreactors and machine learning (ML) that was conducted by researchers from NERS and Idaho National Laboratory. The full paper, “Nuclear Microreactor Transient and Load-Following Control with Deep Reinforcement Learning,” was featured in the July issue of Energy Conversion and Management: X.
K. Natesan, D. L. Smith
Nuclear Technology | Volume 22 | Number 1 | April 1974 | Pages 138-150
Technical Paper | Fusion Reactor Materials / Material | doi.org/10.13182/NT74-A16283
Articles are hosted by Taylor and Francis Online.
Thermodynamic calculations were made on the distribution of hydrogen and tritium between various refractory metals and liquid lithium as a function of temperature. The limiting tritium pressures that can be attained by cold trapping secondary liquid metals such as sodium, potassium, and sodium—78 wt% potassium (NaK) were also calculated. In the absence of tritium breeding, these pressures are 2.5 × 10−5, 2 × 10−7, and 1.2 × 10−10 Torr for sodium, potassium, and NaK, respectively, which correspond to tritium concentrations in lithium of 45, 4, and < 1 ppm, respectively, at 700°C. For a 1000-MW(th) thermonuclear reactor with a tritium breeding rate of 150 g/day, a tritium recovery system that incorporates (a) a separate lithium purification loop with niobium as the permeable membrane, (b) NaK as the secondary heat transport fluid, and (c) tungsten cladding on the IHX tubes will yield a tritium pressure of 10−9 Torr or less in the secondary system. This configuration will result in a tritium release rate ∼10−6 g/h to the steam system for a tungsten-clad steam generator operating at ∼600°C. The corresponding activity release rate is ∼300 Ci/yr.