DyMAS: A Direct Multi-Scale Pore-Level Simulation Approach
Bibliographic record
Abstract
Abstract The recent advancements in high-resolution imaging technology offer the opportunity to generate detailed pore-level domains and highlight the need for efficient and reliable digital core analyzers. Here, a new generation of direct pore-scale simulation techniques called dynamic morphology assisted simulation (DyMAS) is proposed. DyMAS, as a hybrid method, couples pore morphological quasi-static and computational fluid dynamic approaches to simulate immiscible multiphase fluid flow at pore-scale with high computational efficiency. DyMAS is a comprehensive modeling approach that has the capability of dealing with pore structures of a wide range of pore sizes, from intergranular to microporosity, simultaneously. It is a selective approach that updates the governing equations along with the interface development to prevent numerical instabilities associated with the interface reconstruction and volume tracking processes. Gravity, viscous, and capillary forces are all taken into account ensuring accurate simulation of the compound fluid displacement patterns, e.g., splitting and coalescence, viscous fingering, ganglia mobilization, gravity segregation, and capillary trapping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".