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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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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.
Joe E. Dahlquist, Fred S. GL, Ralph A. Nelson
Nuclear Technology | Volume 68 | Number 2 | February 1985 | Pages 252-262
Technical Paper | Fabrication of Components of the Creys-Malville Plant / Heat Transfer and Fluid Flow | doi.org/10.13182/NT85-A33558
Articles are hosted by Taylor and Francis Online.
During normal and accidental operations of a light water nuclear reactor, a wide range of thermal-hydraulic conditions may be encountered for which the critical heat flux (CHF) cannot be predicted by a single correlation. An encompassing model was developed for predicting the steady-state forced convective CHF for water over a wide range of thermal-hydraulic conditions. A CHF model is postulated using a conceptual CHF map to define possible CHF mechanisms for given thermal-hydraulic conditions. Existing steadystate CHF correlations, for which the primary CHF mechanism modeled can be identified, are then used in conjunction with the conceptual CHF map to construct a predictive CHF model. The CHF correlations used as the foundation of this model are the Westinghouse-3, the Biasi, and the Modified-Barnett correlations. These correlations allow coverage of a wide range of thermal-hydraulic conditions, provide favorable comparison with experimental data, and are commonly used in the nuclear industry. The parametric ranges covered by the resultant model are
0.3 < P (MPa) <16.0
6.0 <D (mm) <30.0
100.0 <G (kg/m2·s) < 8000.0
−0.3 <X (dimensionless) < 1.0,where P is pressure; D, the hydraulic diameter; G, the mass flux; and X is quality. The CHF model compares favorably with available experimental data and was used to construct specific CHF maps.
