Revisiting the empirical particle‐fluid coupling model used in DEM‐CFD by high‐resolution DEM‐LBM‐IMB simulations: A 2D perspective

Zeng, Zhixiong; Fu, Jinlong; Feng, Y. T.; Wang, Min

doi:10.1002/nag.3496

Cited by 6 publications

(1 citation statement)

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“…LBM [ 62 , 111 , 119 ] models the fluid flow through a time-dependent distribution of fluid particles propagating on a regular lattice. In DRP research, pore voxels in digital rock images serve as the regular lattice for LBM to simulate pore-scale fluid flow, and each lattice node is located in the center of corresponding pore voxel.…”

Section: Methodology and Data Preparationmentioning

confidence: 99%

A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation

Wang

Chen

et al. 2023

Engineering with Computers

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Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. High-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.

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Section: Methodology and Data Preparationmentioning

confidence: 99%