Numerical simulation of fine particle liquid–solid flow in porous media based on LBM‐IBM‐DEM
Bibliographic record
Abstract
Abstract Fine particle liquid–solid flow in porous media is involved in many industrial processes such as oil exploitation, geothermal reinjection, and filtration systems. It is of great significance to master the behaviours of the fine particle liquid–solid flow in porous media. At present, there are few studies on the influences of the migration of fine particles on the flow field in porous media, and the effects of the porosity of porous media and inlet fluid velocity on the migration behaviours of fine particles in porous media. In this paper, a liquid–solid flow model was established based on the lattice Boltzmann method (LBM)‐immersed boundary method (IBM)‐distinct element method (DEM) and verified by the classical Drag Kiss Tumble (DKT) phenomena and flow around a cylinder successfully. In this model, the interaction between solid particles is analyzed using the distinct element method, and the interaction between fine particles and flow field is handled by IBM. Then, the migration and blockage of fine particles in porous media was studied using this model. It is found that, in addition to the blockage, a large amount of blocked‐surface sliding‐separation occur in fine particles. At the same time, the decrease in porosity increases the damage degree of fine particles on the permeability. The porosity exerts great influence on the penetration rate and dispersion behaviour of fine particles. The inlet fluid velocity mainly affects the residence time of fine particles and the average velocity of motion in the direction perpendicular to the main flow direction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".