Crevillen-Garcia, D., Wilkinson, R.D. orcid.org/0000-0001-7729-7023, Shah, A.A. et al. (1 more author) (2017) Gaussian Process Modelling for Uncertainty Quantification in Convectively-Enhanced Dissolution Processes in Porous Media. Advances in Water Resources, 99. pp. 1-14. ISSN 0309-1708
Abstract
Numerical groundwater flow and dissolution models of physico-chemical processes in deep aquifers are usually subject to uncertainty in one or more of the model input parameters. This uncertainty is propagated through the equations and needs to be quantified and characterised in order to rely on the model outputs. In this paper we present a Gaussian process emulation method as a tool for performing uncertainty quantification in mathematical models for convection and dissolution processes in porous media. One of the advantages of this method is its ability to significantly reduce the computational cost of an uncertainty analysis, while yielding accurate results, compared to classical Monte Carlo methods. We apply the methodology to a model of convectively-enhanced dissolution processes occurring during carbon capture and storage. In this model, the Gaussian process methodology fails due to the presence of multiple branches of solutions emanating from a bifurcation point, i.e., two equilibrium states exist rather than one. To overcome this issue we use a classifier as a precursor to the Gaussian process emulation, after which we are able to successfully perform a full uncertainty analysis in the vicinity of the bifurcation point.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Elsevier Ltd. This is an author produced version of a paper subsequently published in Advances in Water Resources. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Convectively-enhanced dissolution; Partial differential equations with random inputs; Multiple solutions and bifurcation; Gaussian process emulation and classification; Uncertainty quantification |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Nov 2016 09:27 |
Last Modified: | 09 Nov 2017 01:38 |
Published Version: | https://doi.org/10.1016/j.advwatres.2016.11.006 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.advwatres.2016.11.006 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:107174 |