Study on application of artificial neural network to debris bed coolability calculations for sodium-cooled fast reactors

Understanding the effect of uncertainties of Core Disruptive Accident (CDA) scenarios on debris bed coolability on a core catcher is required for decision making on design options to mitigate a CDA consequence. For the understanding, a huge number of calculations are required but are extremely diffi...

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Autores principales:	Eiji MATSUO, Kyohei SASA, Hiroyuki SAITO, Yutaka ABE
Formato:	article
Lenguaje:	EN
Publicado:	The Japan Society of Mechanical Engineers 2020
Materias:	sodium-cooled fast reactor debris bed coolability speed-up calculations artificial neural network machine learning application latin hypercube sampling Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/a47719620ca94f79ab0356c26014b793
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Study on application of artificial neural network to debris bed coolability calculations for sodium-cooled fast reactors

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