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Uncertainty propagation in pore water chemical composition calculation using surrogate models

Performance assessment in deep geological nuclear waste repository systems necessitates an extended knowledge of the pore water chemical conditions prevailing in host-rock formations. In the last two decades, important progress has been made in the experimental characterization and thermodynamic mod...

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Autores principales: Sochala, Pierre, Chiaberge, Christophe, Claret, Francis, Tournassat, Christophe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445106/
https://www.ncbi.nlm.nih.gov/pubmed/36064793
http://dx.doi.org/10.1038/s41598-022-18411-5
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author Sochala, Pierre
Chiaberge, Christophe
Claret, Francis
Tournassat, Christophe
author_facet Sochala, Pierre
Chiaberge, Christophe
Claret, Francis
Tournassat, Christophe
author_sort Sochala, Pierre
collection PubMed
description Performance assessment in deep geological nuclear waste repository systems necessitates an extended knowledge of the pore water chemical conditions prevailing in host-rock formations. In the last two decades, important progress has been made in the experimental characterization and thermodynamic modeling of pore water speciation, but the influence of experimental artifacts and uncertainties of thermodynamic input parameters are seldom evaluated. In this respect, we conducted an uncertainty propagation study in a reference geochemical model describing the pore water chemistry of the Callovian-Oxfordian clay formation. Nineteen model input parameters were perturbed, including those associated to experimental characterization (leached anions, exchanged cations, cation exchange selectivity coefficients) and those associated to generic thermodynamic databases (solubilities). A set of 13 quantities of interest were studied by the use of polynomial chaos expansions built non-intrusively with a least-squares forward stepwise regression approach. Training and validation sets of simulations were carried out using the geochemical speciation code PHREEQC. The statistical results explored the marginal distribution of each quantity of interest, their bivariate correlations as well as their global sensitivity indices. The influence of the assumed distributions for input parameters uncertainties was evaluated by considering two parametric domain sizes.
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spelling pubmed-94451062022-09-07 Uncertainty propagation in pore water chemical composition calculation using surrogate models Sochala, Pierre Chiaberge, Christophe Claret, Francis Tournassat, Christophe Sci Rep Article Performance assessment in deep geological nuclear waste repository systems necessitates an extended knowledge of the pore water chemical conditions prevailing in host-rock formations. In the last two decades, important progress has been made in the experimental characterization and thermodynamic modeling of pore water speciation, but the influence of experimental artifacts and uncertainties of thermodynamic input parameters are seldom evaluated. In this respect, we conducted an uncertainty propagation study in a reference geochemical model describing the pore water chemistry of the Callovian-Oxfordian clay formation. Nineteen model input parameters were perturbed, including those associated to experimental characterization (leached anions, exchanged cations, cation exchange selectivity coefficients) and those associated to generic thermodynamic databases (solubilities). A set of 13 quantities of interest were studied by the use of polynomial chaos expansions built non-intrusively with a least-squares forward stepwise regression approach. Training and validation sets of simulations were carried out using the geochemical speciation code PHREEQC. The statistical results explored the marginal distribution of each quantity of interest, their bivariate correlations as well as their global sensitivity indices. The influence of the assumed distributions for input parameters uncertainties was evaluated by considering two parametric domain sizes. Nature Publishing Group UK 2022-09-05 /pmc/articles/PMC9445106/ /pubmed/36064793 http://dx.doi.org/10.1038/s41598-022-18411-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Sochala, Pierre
Chiaberge, Christophe
Claret, Francis
Tournassat, Christophe
Uncertainty propagation in pore water chemical composition calculation using surrogate models
title Uncertainty propagation in pore water chemical composition calculation using surrogate models
title_full Uncertainty propagation in pore water chemical composition calculation using surrogate models
title_fullStr Uncertainty propagation in pore water chemical composition calculation using surrogate models
title_full_unstemmed Uncertainty propagation in pore water chemical composition calculation using surrogate models
title_short Uncertainty propagation in pore water chemical composition calculation using surrogate models
title_sort uncertainty propagation in pore water chemical composition calculation using surrogate models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445106/
https://www.ncbi.nlm.nih.gov/pubmed/36064793
http://dx.doi.org/10.1038/s41598-022-18411-5
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