The estimation of permeability of a porous medium with a generalized pore structure by geometry identification
Bibliographic record
Abstract
The paper presents an approach for estimating the permeability of a porous medium that is based on the characteristics of the porous structure. The pressure drop in different fluid flow passages is estimated and these are combined to evaluate the overall reduction. The theory employed is presented and the level of accuracy for different cases is discussed. The successive steps in the solution algorithm are described. The accuracy and computational efficiency of the approach are compared with results obtained from a finite-element-based multiphysics formulation. It is shown that for a comparable accuracy, the computational efficiency of the approach can be two orders of magnitude faster. Finally, the model predictions are examined with conventional relationships that have been reported in the literature and are based on permeability-porosity relationships. It is shown that estimating the permeability of a porous medium using porosity can lead to an order of magnitude error and the expected permeability range in different porosities is presented using 10 000 random structures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".