Analytical modeling nanoparticles‐fines reactive transport in porous media saturated with mobile immiscible fluids
Bibliographic record
Abstract
Abstract This work develops a new analytical solution for the reactive flow of aqueous nanofluid into porous media originally saturated with a mobile aqueous suspension containing fine particles and a mobile oleic‐phase. The enhancement of nanoparticles on fines attachment onto rock grains is modeled through the increase of the maximum retention capacity of rock grains. Implementing the splitting technique and stream‐function converts the original 3 × 3 system of partial‐differential equations into 2 × 2 sub‐system of nanoparticles‐fines reactive transport, and a lifting equation where only phase saturation appears. Then, method of characteristics is applied to achieve the analytical solution, the validity of which is tested by numerical simulation. The historical profiles of suspended/adsorbed nanoparticles, suspended/attached fines, and phase saturation along 1‐D porous medium are reproduced. The impact of injected nanoparticles concentration and carrier fluid saturation on fines attachment is investigated. This work provides a simple‐yet‐rigorous approach to evaluate nanofluid injection to control fines migration in multiphase flow.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".