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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spelling	oai:doaj.org-article:a47719620ca94f79ab0356c26014b7932021-11-29T05:56:30ZStudy on application of artificial neural network to debris bed coolability calculations for sodium-cooled fast reactors2187-974510.1299/mej.19-00541https://doaj.org/article/a47719620ca94f79ab0356c26014b7932020-03-01T00:00:00Zhttps://www.jstage.jst.go.jp/article/mej/7/3/7_19-00541/_pdf/-char/enhttps://doaj.org/toc/2187-9745Understanding 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 difficult to perform because a huge number of calculations require much calculation time to solve non-steady equations in the coolability calculation model. Thus, we applied Artificial Neural Network (ANN), which is one of models for machine learning, to debris bed coolability calculations. The application of ANN is expected to exponentially improve the calculation speed of debris bed coolability because ANN provides results from experimental rules learned through training without solving non-steady equations. The application is in three steps. Firstly, we created many data for training ANN and validating the trained ANN through coolability calculations parameterizing main dominant inputs (particle diameter of debris bed, porosity of debris bed, etc.) by using Latin hypercube sampling. Secondly, ANN was trained and validated with the created data. The accuracy rate of the results by the ANN to the validation data exceeded 99%. In addition, the calculation time using ANN was micro seconds order. Finally, through demonstration calculations, it was confirmed that we can easily understand the effect of uncertainties of CDA scenarios on debris bed coolability owing to results visualization based on a huge number of parametric calculations using ANN. Thus, the application of ANN to debris bed coolability calculations should contribute to the decision making on design options to mitigate a CDA consequence.Eiji MATSUOKyohei SASAHiroyuki SAITOYutaka ABEThe Japan Society of Mechanical Engineersarticlesodium-cooled fast reactordebris bed coolabilityspeed-up calculationsartificial neural networkmachine learningapplicationlatin hypercube samplingMechanical engineering and machineryTJ1-1570ENMechanical Engineering Journal, Vol 7, Iss 3, Pp 19-00541-19-00541 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	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
spellingShingle	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 Eiji MATSUO Kyohei SASA Hiroyuki SAITO Yutaka ABE Study on application of artificial neural network to debris bed coolability calculations for sodium-cooled fast reactors
description	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 difficult to perform because a huge number of calculations require much calculation time to solve non-steady equations in the coolability calculation model. Thus, we applied Artificial Neural Network (ANN), which is one of models for machine learning, to debris bed coolability calculations. The application of ANN is expected to exponentially improve the calculation speed of debris bed coolability because ANN provides results from experimental rules learned through training without solving non-steady equations. The application is in three steps. Firstly, we created many data for training ANN and validating the trained ANN through coolability calculations parameterizing main dominant inputs (particle diameter of debris bed, porosity of debris bed, etc.) by using Latin hypercube sampling. Secondly, ANN was trained and validated with the created data. The accuracy rate of the results by the ANN to the validation data exceeded 99%. In addition, the calculation time using ANN was micro seconds order. Finally, through demonstration calculations, it was confirmed that we can easily understand the effect of uncertainties of CDA scenarios on debris bed coolability owing to results visualization based on a huge number of parametric calculations using ANN. Thus, the application of ANN to debris bed coolability calculations should contribute to the decision making on design options to mitigate a CDA consequence.
format	article
author	Eiji MATSUO Kyohei SASA Hiroyuki SAITO Yutaka ABE
author_facet	Eiji MATSUO Kyohei SASA Hiroyuki SAITO Yutaka ABE
author_sort	Eiji MATSUO
title	Study on application of artificial neural network to debris bed coolability calculations for sodium-cooled fast reactors
title_short	Study on application of artificial neural network to debris bed coolability calculations for sodium-cooled fast reactors
title_full	Study on application of artificial neural network to debris bed coolability calculations for sodium-cooled fast reactors
title_fullStr	Study on application of artificial neural network to debris bed coolability calculations for sodium-cooled fast reactors
title_full_unstemmed	Study on application of artificial neural network to debris bed coolability calculations for sodium-cooled fast reactors
title_sort	study on application of artificial neural network to debris bed coolability calculations for sodium-cooled fast reactors
publisher	The Japan Society of Mechanical Engineers
publishDate	2020
url	https://doaj.org/article/a47719620ca94f79ab0356c26014b793
work_keys_str_mv	AT eijimatsuo studyonapplicationofartificialneuralnetworktodebrisbedcoolabilitycalculationsforsodiumcooledfastreactors AT kyoheisasa studyonapplicationofartificialneuralnetworktodebrisbedcoolabilitycalculationsforsodiumcooledfastreactors AT hiroyukisaito studyonapplicationofartificialneuralnetworktodebrisbedcoolabilitycalculationsforsodiumcooledfastreactors AT yutakaabe studyonapplicationofartificialneuralnetworktodebrisbedcoolabilitycalculationsforsodiumcooledfastreactors
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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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