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Division Spotlight
Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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
Kentucky legislature sends nuclear bills to governor
Kentucky’s Republican-majority legislature passed a bill this past week that could bring nuclear energy to the “coal-is-king” state as lawmakers broadly seek solutions to reduce carbon emissions. The bill went to Democratic Gov. Andrew Beshear on Monday for final approval.
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.