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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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Latest News
Nano Nuclear wins Air Force contract for Kronos MMR
New York City–based advanced nuclear technology developer Nano Nuclear Energy has been awarded a Direct-to-Phase II Small Business Innovation Research contract for its Kronos micro modular reactor (MMR) by AFWERX, the innovation and venture arm of the U.S. Air Force. The contract calls for AFWERX, with the 11th Civil Engineering Squadron, to explore the feasibility of deploying the Kronos MMR Energy System at Joint Base Anacostia-Bolling (JBAB) in Washington, D.C.
Andreas Ikonomopoulos, Miltiadis Alamaniotis, Stylianos Chatzidakis, Lefteri H. Tsoukalas
Nuclear Technology | Volume 182 | Number 1 | April 2013 | Pages 1-12
Technical Paper | Fission Reactors | doi.org/10.13182/NT13-A15821
Articles are hosted by Taylor and Francis Online.
A novel machine learning approach for nuclear power plant modeling and state identification is presented together with its test results using data from the Loss-of-Fluid Test experimental facility. The approach exploits Gaussian processes whose principal function is to tackle the temporal problem of forecasting the actual system state in the varying environment of a nuclear reactor facility that undergoes successive overcooling transients. The approach fuses independent Gaussian process expert predictions to provide a single recommendation to the plant operators in a form that is suitable to appear on a decision support system screen. A variety of test cases are developed to explore the validity and relevance of Gaussian processes. The proposed implementation is examined with various predictor variables under different conditions, and the results obtained are in accordance with model expectations.