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

TL;DR

This paper demonstrates that the on-demand machine learning algorithm significantly accelerates reactive transport simulations in heterogeneous porous media by coupling Reaktoro and Firedrake, achieving speed-ups of one to three orders of magnitude.

Contribution

It applies and evaluates the ODML algorithm to reactive transport problems, showing substantial computational speed-ups in coupled geochemical and flow simulations.

Findings

ODML accelerates geochemical calculations by 10 to 1000 times.

Coupling Reaktoro and Firedrake enables efficient reactive transport modeling.

Significant reduction in overall simulation time achieved.

Abstract

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).