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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.
Muhammad Yousaf (Purdue Univ), Shoaib Usman (Missouri S&T)
Proceedings | Advances in Thermal Hydraulics 2018 | Orlando, FL, November 11-15, 2018 | Pages 1091-1099
A lattice Boltzmann method was utilized to investigate the natural convection heat transfer in the presence of sinusoidal roughness elements in a two-dimensional rectangular cavity heated at the bottom. Coupled momentum and energy equations were solved in a two-dimensional lattice using the single relaxation time Bhatnagar-Gross-Krook (BGK) model of lattice Boltzmann method. Computational model was validated against the previous benchmark solutions and a very good agreement was found to exist with smooth and rough cavities. Numerical studies were performed for a Newtonian fluid of the Prandtl number (Pr) 1.0 in a cavity of aspect ratio (L/H) 2.0. Sinusoidal roughness elements (n = 08) were placed on hot, cold, and both the hot and cold walls simultaneously. The dimensionless amplitude was varied from 0.015 to 0.15 in small steps. The number of the roughness elements was held constant to investigate the Rayleigh numbers (Ra) between 1x103 and 1x106. The computational results showed that a small roughness amplitude of approximately 0.025 has no significant effects on the average heat transfer. In contrast, the presence of sinusoidal roughness with an amplitude ? 0.05 causes the average heat transfer to degrade and delay in the onset of the natural circulation.