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Parametric study of hydrogenic inventory in the ITER divertor based on machine learning
A parametric study is performed with the 2D FESTIM code for the ITER monoblock geometry. The influence of the monoblock surface temperature, the incident ion energy and particle flux on the monoblock hydrogen inventory is investigated. The simulated data is analysed with a Gaussian regression proces...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7576168/ https://www.ncbi.nlm.nih.gov/pubmed/33082471 http://dx.doi.org/10.1038/s41598-020-74844-w |
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author | Delaporte-Mathurin, Rémi Hodille, Etienne Mougenot, Jonathan De Temmerman, Gregory Charles, Yann Grisolia, Christian |
author_facet | Delaporte-Mathurin, Rémi Hodille, Etienne Mougenot, Jonathan De Temmerman, Gregory Charles, Yann Grisolia, Christian |
author_sort | Delaporte-Mathurin, Rémi |
collection | PubMed |
description | A parametric study is performed with the 2D FESTIM code for the ITER monoblock geometry. The influence of the monoblock surface temperature, the incident ion energy and particle flux on the monoblock hydrogen inventory is investigated. The simulated data is analysed with a Gaussian regression process and an inventory map as a function of ion energy and incident flux is given. Using this inventory map, the hydrogen inventory in the divertor is easily derived for any type of scenario. Here, the case of a detached ITER scenario with inputs from the SOLPS code is presented. For this scenario, the hydrogen inventory per monoblock is highly dependent of surface temperature and ranges from [Formula: see text] to [Formula: see text] H after a [Formula: see text] s exposure. The inventory evolves as a power law of time and is lower at strike points where the surface temperature is high. Hydrogen inventory in the whole divertor after a [Formula: see text] s exposure is estimated at approximately 8 g. |
format | Online Article Text |
id | pubmed-7576168 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75761682020-10-21 Parametric study of hydrogenic inventory in the ITER divertor based on machine learning Delaporte-Mathurin, Rémi Hodille, Etienne Mougenot, Jonathan De Temmerman, Gregory Charles, Yann Grisolia, Christian Sci Rep Article A parametric study is performed with the 2D FESTIM code for the ITER monoblock geometry. The influence of the monoblock surface temperature, the incident ion energy and particle flux on the monoblock hydrogen inventory is investigated. The simulated data is analysed with a Gaussian regression process and an inventory map as a function of ion energy and incident flux is given. Using this inventory map, the hydrogen inventory in the divertor is easily derived for any type of scenario. Here, the case of a detached ITER scenario with inputs from the SOLPS code is presented. For this scenario, the hydrogen inventory per monoblock is highly dependent of surface temperature and ranges from [Formula: see text] to [Formula: see text] H after a [Formula: see text] s exposure. The inventory evolves as a power law of time and is lower at strike points where the surface temperature is high. Hydrogen inventory in the whole divertor after a [Formula: see text] s exposure is estimated at approximately 8 g. Nature Publishing Group UK 2020-10-20 /pmc/articles/PMC7576168/ /pubmed/33082471 http://dx.doi.org/10.1038/s41598-020-74844-w Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Delaporte-Mathurin, Rémi Hodille, Etienne Mougenot, Jonathan De Temmerman, Gregory Charles, Yann Grisolia, Christian Parametric study of hydrogenic inventory in the ITER divertor based on machine learning |
title | Parametric study of hydrogenic inventory in the ITER divertor based on machine learning |
title_full | Parametric study of hydrogenic inventory in the ITER divertor based on machine learning |
title_fullStr | Parametric study of hydrogenic inventory in the ITER divertor based on machine learning |
title_full_unstemmed | Parametric study of hydrogenic inventory in the ITER divertor based on machine learning |
title_short | Parametric study of hydrogenic inventory in the ITER divertor based on machine learning |
title_sort | parametric study of hydrogenic inventory in the iter divertor based on machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7576168/ https://www.ncbi.nlm.nih.gov/pubmed/33082471 http://dx.doi.org/10.1038/s41598-020-74844-w |
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