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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.
W. C. Rivard, J. R. Travis
Nuclear Science and Engineering | Volume 74 | Number 1 | April 1980 | Pages 40-48
Technical Paper | doi.org/10.13182/NSE80-A18945
Articles are hosted by Taylor and Francis Online.
A new model is described for nonequilibrium vapor production (flashing) in critical two-phase flow. The model is based on a description of turbulence enhanced thermal diffusivity in the liquid and a Weber number criterion for bubble size. In a quiescent environment, the model reduces to the well-known conduction controlled rate. Results of calculations are compared with flow rate and pressure data from blowdown experiments with various nozzle geometries. The nozzle throat diameters range from 1.8 to 51.0 cm and nozzle inlet conditions vary from water subcooled 30°C to saturated water at 98% vapor volume fraction. The calculations are made with the two-fluid code K-FIX and show very good agreement throughout the entire blowdown.