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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.
Heinrich Werle
Nuclear Technology | Volume 59 | Number 1 | October 1982 | Pages 160-164
Technical Paper | Heat Transfer and Fluid Flow | doi.org/10.13182/NT82-A33061
Articles are hosted by Taylor and Francis Online.
In connection with investigations concerning the core melt/concrete interaction, the enhancement of heat transfer between two horizontal liquid layers by gas injection has been studied using two systems—oil over water and oil over Wood’s metal—with very different density ratios. For the largest gas injection rate (superficial gas velocity 6.3×10−3 m/s), the heat transfer coefficient is increased by a factor of nearly 400 for oil over water and by a factor of ∼10 for oil over Wood’s metal In the core melt/concrete interaction, the superficial gas velocities might be even higher; therefore, the gas-induced enhancement of interfacial heat transfer should be taken into account.