Pore network modelling of molecular diffusion in a single‐block model during lean gas injection, a comparative study on calculation approaches
Bibliographic record
Abstract
Abstract In this paper, we have proposed a new algorithm for pore network modelling of molecular diffusion in a single matrix block model. The block contains a volatile component adjacent to a gaseous lean component to mimic high capillarity‐induced flow rates caused by either high liquid to gas mass transfer rates and/or small throat size of the porous medium. Various types of boundary conditions for liquid pressure calculation are formulated. Advantages and the range of applicability of this algorithm are compared against an existing algorithm. A threshold value for surface mass transfer coefficient is obtained above which the algorithms that are based on the rule that only one bond is invaded at each step of invasion are no longer applicable. Simultaneous multiple throat invasion in a single time step, unnecessity of liquid cluster identification, and possibility of throat refilling are the major advantages of the new pressure‐based algorithm. While the phase distribution in the old approach is insensitive to surface mass transfer coefficient the proposed approach is severely dependent on the magnitude of this coefficient. The incident in which the liquid body is detached from the top surface in the proposed algorithm is also a strong function of surface mass transfer coefficient. The advantages of the new algorithm, however, come with a higher computational cost, especially for large network sizes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".