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This work investigates the performance of the on-demand machine learning(ODML) algorithm introduced in Leal et al. (2020) when applied to differentreactive transport problems in heterogeneous porous media. ODML was devised toaccelerate the computationally expensive geochemical reaction calculations inreactive transport simulations. We demonstrate that the ODML algorithm speedsup these calculations by one to three orders of magnitude. Such acceleration,in turn, significantly accelerates the entire reactive transport simulation.The numerical experiments are performed by implementing the coupling of twoopen-source software packages: Reaktoro (Leal, 2015) and Firedrake (Rathgeberet al., 2016).

Accelerated reactive transport simulations in heterogeneous porous media using Reaktoro and Firedrake