Multiscale numerical simulation of flow in porous media using 3D digital twin models from computed tomography
Bibliographic record
Abstract
Over the past decade, advancements in the numerical resolution of the Navier-Stokes equations for multiphase flow modeling have enabled the study of key transport processes in porous media while accounting for non-Darcian effects, including inertial, boundary, and variable porosity influences. Simultaneously, improvements in post-processing techniques for CT-scan images (such as reconstruction and segmentation) applied to porous material samples have made the process increasingly reliable. As a result, integrating real and complex geometries from CT scans into numerical simulations has become a cutting-edge topic in scientific research. This study explores different numerical approaches that utilize Computed Tomography (CT) data for porous media modeling, focusing on intricate couplings between flow dynamics and fluid-solid interactions. We present a workflow for generating a 3D numerical model from CT-scan images of porous media for Computational Fluid Dynamics (CFD) simulations. The uncertainties in porosity and permeability are assessed using CT-scan datasets of a reference material—assemblies of monodisperse glass beads—for which analytical and empirical solutions are available in the literature. Meshes are generated using the open-source platform Salome, and numerical simulations are conducted with Code_Saturne, a multiphysics CFD software based on a finite-volume approach, which supports arbitrary cell types and grid structures. The resolution of CT-scan data acquisition and the selection of image post-processing filters play a crucial role in the accuracy of numerical solutions. Notably, the reconstruction of pore space interfaces alters surface and volume representations, significantly affecting porosity and, consequently, permeability calculations. This study provides a detailed uncertainty analysis for an ideal porous medium, serving as a reference framework for the development of a multiscale and multiphysics methodology applicable to various particle assemblies, including geomaterials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".