A Mathematical Modelling of Solid Particles Transport in Two-Zone Porous Media with Non-Linear Kinetics of Deposition
Bibliographic record
Abstract
The processes of transport of various pollutants in porous media are of great practical importance.Such contaminants can be solid colloidal particles suspended in the carrier fluid.In the process of transfer, particles can be deposited in pores, which significantly change the permeability and porosity characteristics of the medium.The heterogeneity of porous media considerably affects the transfer of these particles.One of the macroscopically inhomogeneous media is zonal inhomogeneous media, consisting of several zones with different characteristics.In such media, generalized mathematical models have not yet been developed that take into account the zonal inhomogeneity of the medium, various linear and nonlinear, reversible and irreversible kinetics of deposition of solid particles from the liquid into the pore space, etc.In this work, a model is generalized for the transfer of solid particles in a two-zone porous medium.In this work, a mathematical model is considered for colloidal particles transport process in a two-zone porous medium and both the zones having the reversible retentions of particles with different characteristics (parameters).It is shown that the nonlinear kinetics of particle deposition, other parameters being equal, leads to an intensification of particle deposition in pores.As the index n decreases from unity, the rate of particle deposition increases in both zones of the medium.As a consequence, the concentration of suspended particles in the mobile fluid in both zones decreases.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 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".