ArticleACS omega2023
Coupled Lattice Boltzmann Modeling Framework for Pore-Scale Fluid Flow and Reactive Transport.
Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Pore-Scale Mechanisms of COACS omega · 2026Article
- Pore-Scale Wettability Alteration in Green Surfactant Flooding: A Lattice Boltzmann Study on Contact Angle Dynamics.ACS omega · 2026Article
- Adaptive Time-Stepping and Machine Learning Approach for Pore-Scale Reactive Transport Simulations.Transport in porous media · 2026Article
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Authors and funding
4 authors.
Funding
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Abstract
In this paper, we propose a modeling framework for pore-scale fluid flow and reactive transport based on a coupled lattice Boltzmann model (LBM). We develop a modeling interface to integrate the LBM modeling code parallel lattice Boltzmann solver and the PHREEQC reaction solver using multiple flow and reaction cell mapping schemes. The major advantage of the proposed workflow is the high modeling flexibility obtained by coupling the geochemical model with the LBM fluid flow model. Consequently, the model is capable of executing one or more complex reactions within desired cells while preserving the high data communication efficiency between the two codes. Meanwhile, the developed mapping mechanism enables the flow, diffusion, and reactions in complex pore-scale geometries. We validate the coupled code in a series of benchmark numerical experiments, including 2D single-phase Poiseuille flow and diffusion, 2D reactive transport with calcite dissolution, as well as surface complexation reactions. The simulation results show good agreement with analytical solutions, experimental data, and multiple other simulation codes. In addition, we design an AI-based optimization workflow and implement it on the surface complexation model to enable increased capacity of the coupled modeling framework. Compared to the manual tuning results proposed in the literature, our workflow demonstrates fast and reliable model optimization results without incorporating pre-existing domain knowledge.
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Registered trials
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