Crevillén-García, D., Wilkinson, R. D., Shah, Akeel A. and Power, H. (2017) Gaussian process modelling for uncertainty quantification in convectively-enhanced dissolution processes in porous media. Advances in Water Resources, 99 . pp. 1-14. doi:10.1016/j.advwatres.2016.11.006

Preview

PDF WRAP-Gaussian-process-modelling-uncertainty-quantification-Crevillen-Garcia-2017.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1324Kb) | Preview

Request Changes to record.

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.

Item Type:	Journal Article
Subjects:	G Geography. Anthropology. Recreation > GB Physical geography Q Science > QA Mathematics
Divisions:	Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH):	Groundwater flow , Uncertainty -- Mathematical models, Gaussian processes, Differential equations, Partial
Journal or Publication Title:	Advances in Water Resources
Publisher:	Pergamon Press
ISSN:	0309-1708
Official Date:	January 2017
Dates:	Date Event January 2017 Published 9 November 2016 Available 7 November 2016 Accepted 23 May 2016 Submitted
Volume:	99
Page Range:	pp. 1-14
DOI:	10.1016/j.advwatres.2016.11.006
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Seventh Framework Programme (European Commission) (FP7)
Grant number:	Grant agreement 282900

Request changes or add full text files to a record

Repository staff actions (login required)

View Item

Downloads