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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Latest News
Lightbridge to test uranium-zirconium fuel alloy in INL’s ATR
Lightbridge Corporation has fabricated samples of nuclear fuel materials made of an enriched uranium-zirconium alloy, matching the composition of the alloy that the company intends to use for its future commercial Lightbridge Fuel product. The fuel is designed to improve the performance, safety, and proliferation resistance of nuclear reactors, according to the company. The enriched coupon samples will now be placed into capsules for irradiation testing in Idaho National Laboratory’s Advanced Test Reactor.
François Bachoc, Karim Ammar, Jean-Marc Martinez
Nuclear Science and Engineering | Volume 183 | Number 3 | July 2016 | Pages 387-406
Technical Paper | doi.org/10.13182/NSE15-108
Articles are hosted by Taylor and Francis Online.
It is now common practice in nuclear engineering to base extensive studies on numerical computer models. These studies require running computer codes in potentially thousands of numerical configurations and without expert individual controls on the computational and physical aspects of each simulation. In this paper, we compare different statistical metamodeling techniques and show how metamodels can help improve the global behavior of codes in these extensive studies. We consider the metamodeling of the Germinal thermomechanical code by Kriging, kernel regression, and neural networks. Kriging provides the most accurate predictions, while neural networks yield the fastest metamodel functions. All three metamodels can conveniently detect strong computation failures. However, it is more challenging to detect code instabilities, that is, groups of computations that are all valid but numerically inconsistent with one another. For code instability detection, we find that Kriging provides an interesting tool.