Impact of space‐time mesh adaptation on solute transport modeling in porous media. Issue 2 (27th February 2015)

• Record Type: Journal Article
• Authors: Esfandiar, Bahman; Porta, Giovanni; Perotto, Simona; Guadagnini, Alberto
• Is Part Of: Water resources research. Volume 51:Issue 2(2015:Feb.)
• Page Start: 1315
• Page End: 1332
• Publication Date: 2015-02-27
• Subjects: solute transport -- porous media -- mesh adaptation -- time step adaptation -- model calibration -- uncertainty quantification; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2014WR016569
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: We implement a space‐time grid adaptation procedure to efficiently improve the accuracy of numerical simulations of solute transport in porous media in the context of model parameter estimation. We focus on the Advection Dispersion Equation (ADE) for the interpretation of nonreactive transport experiments in laboratory‐scale heterogeneous porous media. When compared to a numerical approximation based on a fixed space‐time discretization, our approach is grounded on a joint automatic selection of the spatial grid and the time step to capture the main (space‐time) system dynamics. Spatial mesh adaptation is driven by an anisotropic recovery‐based error estimator which enables us to properly select the size, shape, and orientation of the mesh elements. Adaptation of the time step is performed through an ad hoc local reconstruction of the temporal derivative of the solution via a recovery‐based approach. The impact of the proposed adaptation strategy on the ability to provide reliable estimates of the key parameters of an ADE model is assessed on the basis of experimental solute breakthrough data measured following tracer injection in a nonuniform porous system. Model calibration is performed in a Maximum Likelihood (ML) framework upon relying on the representation of the ADE solution through a generalized Polynomial Chaos Expansion (gPCE). Our results show that the proposed anisotropic space‐time grid adaptation leads to ML parameter estimates and to model results of markedly improved quality when compared to classical inversion approaches based on a uniform space‐time discretization. Key Points: We interpret solute transport experiment by a space‐time adaptive methodology Adaptive method improves the quality of ML estimates of model dispersivities Discretization strategy markedly affects propagation of parametric uncertainty