Accelerated reactive transport simulations in heterogeneous porous media using Reaktoro and Firedrake. (English) Zbl 1485.86005

Summary: This work investigates the performance of the on-demand machine learning (ODML) algorithm introduced in [the fourth author et al., “Accelerating reactive transport modeling: on-demand machine learning algorithm for chemical equilibrium calculations”, Transp. Porous Media 133, No. 2, 161–204 (2020; doi:10.1007/s11242-020-01412-1)] when applied to different reactive transport problems in heterogeneous porous media. This approach was devised to accelerate the computationally expensive geochemical reaction calculations in reactive transport simulations. We demonstrate that even with a strong heterogeneity present, the ODML algorithm speeds up these calculations by one to three orders of magnitude. Such acceleration, in turn, significantly advances the entire reactive transport simulation. The performed numerical experiments are enabled by the novel coupling of two open-source software packages: Reaktoro [the fourth author, “Reaktoro: An open-source unified framework for modeling chemically reactive systems” (2015), https://reaktoro.org] and Firedrake [F. Rathgeber et al., ACM Trans. Math. Softw. 43, No. 3, Article No. 24, 27 p. (2017; Zbl 1396.65144)]. The first library provides the most recent version of the ODML approach for the chemical equilibrium calculations, whereas, the second framework includes the newly implemented conservative Discontinuous Galerkin finite element scheme for the Darcy problem, i.e., the Stabilized Dual Hybrid Mixed(SDHM) method [Y. Núñez et al., “A Mixed-Hybrid Finite Element Method Applied to Tracer Injection Processes”, Int. J. Model. Simul. Petroleum Ind., 6, 51–59 (2012)].


86A05 Hydrology, hydrography, oceanography
68T05 Learning and adaptive systems in artificial intelligence
76S05 Flows in porous media; filtration; seepage
76V05 Reaction effects in flows


Zbl 1396.65144
Full Text: DOI


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