Pore network modelling of molecular diffusion in a single‐block model during lean gas injection, investigating the effect of throat sorting
Bibliographic record
Abstract
Based on analogy between isothermal drying of porous media and molecular diffusion in a single‐block model containing a volatile liquid during lean gas injection, a detailed procedure is presented for two‐dimensional quasi‐static pore network modelling of diffusion. A modified invasion percolation algorithm is used to account for evaporation of liquid clusters formed during gas injection process. Diffusion in gas phase, capillary‐induced flow in liquid phase, and evaporation in gas‐liquid interface were modelled to trace the movement of gas‐liquid interface and desaturation profile. Using a set of throat sizes with normal distribution, 15 cases of a regular pore network model were proposed by rearranging throat positions in the model. Results of this study indicate that diffusion time, breakthrough time, vapour partial pressure gradient, liquid saturation gradient, liquid cluster frequency, gas‐liquid interface frequency, and evaporation potential are strong functions of throat sorting. Correlation coefficient of throat size with position was used to capture the disorder of porous medium. It was shown that such a correlation coefficient is not capable of describing diffusion characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".