A new flow-kinematics-based model for time-dependent effective dispersion in mixing-limited reactions

TL;DR

This paper introduces a novel 1D upscaling model for 2D mixing-limited reactive transport that captures early-time flow-driven dispersion and late-time Taylor dispersion effects, improving computational efficiency and accuracy.

Contribution

The paper develops a new flow-kinematics-based upscaling method that differentiates between interface spreading and mixing, incorporating a time-dependent effective dispersion term with minimal free parameters.

Findings

Highly accurate for reaction conversion factors

Reasonable spatial distribution of reaction occurrence

Valid for non-reacting systems, enabling calibration

Abstract

A new upscaling procedure that provides 1D representations of 2D mixing-limited reactive transport systems is developed and applied. A key complication with upscaled models in this setting is that the procedure must differentiate between interface spreading, driven by the spatially variable velocity field, and mixing, in which components contact one another and react. Our model captures the enhanced mixing caused by spreading through use of a time-dependent effective dispersion term. The early-time behavior of this dispersion is driven by flow kinematics, while at late times it reaches a Taylor-dispersion-like limit. The early-time behavior is modeled here using a very fast (purely advective) particle tracking procedure, while late-time effects are estimated from scaling arguments. The only free parameter in the model is the asymptotic effective dispersion. This quantity is determined for a few cases by calibrating 1D results to reference 2D results. For most cases, it is estimated using a fit involving a dimensionless grouping of system variables. Numerical results for bimolecular reaction systems are generated using a pseudo-spectral approach capable of resolving fronts at high Peclet numbers. Results are presented for two different types of 2D velocity fields over a wide range of parameters. The upscaled model is shown to provide highly accurate results for conversion factor, along with reasonable approximations of the spatial distribution of reaction occurrence. The model is also shown to be valid for non-reacting systems, and results for such cases can be used in the calibration step to achieve computational savings.