A scalable parallel algorithm for reactive particle tracking

Abstract

Modeling chemical reactions on the basis of probabilistic rules has been employed in Lagrangian schemes to simulate transport of solutes in porous media. Reactive random walk particle tracking allows reagents particle pairs to interact proportionally to their separation distance and the nature of the chemical process. However, most of the available algorithms result to be computationally prohibitive, since their efficiency depends on the number of pairs of particles interacting. In this work, a novel framework for modeling reactive solute transport is introduced, with the target of reducing the computational burden characteristic of existing approaches. Our method introduces an innovative optimal kernel function and permits the parallelization of a bimolecular reaction modeling scheme which leads to computational speedup. To test the performance of our method, we execute a computational speedup analysis with other approaches reported in the literature. The proposed method is successfully validated with exact analytical solutions. We illustrate the applicability of the method to simulate reactive transport in two-dimensional flow fields that are relevant for environmental applications. The computational time of the proposed methodology scales linearly with the number of particles employed in the simulation, feature that makes the algorithm suitable for simulating reactive transport with large number of particles.