Accelerated reactive transport simulations in heterogeneous porous media using Reaktoro and Firedrake
This addresses computational bottlenecks in reactive transport simulations for porous media applications, but is incremental as it builds on an existing method.
This work applied the on-demand machine learning (ODML) algorithm to accelerate geochemical reaction calculations in reactive transport simulations for heterogeneous porous media, demonstrating speedups of one to three orders of magnitude.
This work investigates the performance of the on-demand machine learning (ODML) algorithm introduced in Leal et al. (2020) when applied to different reactive transport problems in heterogeneous porous media. ODML was devised to accelerate the computationally expensive geochemical reaction calculations in reactive transport simulations. We demonstrate that the ODML algorithm speeds up 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 two open-source software packages: Reaktoro (Leal, 2015) and Firedrake (Rathgeber et al., 2016).