On the Importance of Gravity in DNAPL Invasion of Saturated Horizontal Fractures
Bibliographic record
Abstract
Abstract Invasion percolation (IP) models of dense non‐aqueous phase liquid (DNAPL) invasion into saturated horizontal fractures typically neglect viscous and gravity forces, as it is assumed that capillarity dominates in many situations. An IP model simulating DNAPL invasion into saturated horizontal fractures was modified to include gravity as a local effect. The model was optimized using a genetic algorithm, and demonstrated that the inclusion of gravity is important for replicating the architecture of the DNAPL invasion pattern. The optimized gravity‐included simulation showed the DNAPL invasion pattern to be significantly more representative of the experimentally observed pattern (80% accuracy) than did the optimized gravity‐neglected simulation (70% accuracy). Additional simulations of DNAPL invasion in 360 randomly generated fractures were compared with and without gravity forces. These simulations showed that with increasing fracture roughness, the minimum difference between simulations with and without gravity increases to 35% for a standard deviation of the mid‐aperture elevation field (SD z ) of 10 mm. Even for low roughness (SD z = 0.1 mm), the difference was as high as 30%. Furthermore, a scaled Bond Number is defined which includes data regarding DNAPL type, media type and statistical characteristics of the fracture. The value of this scaled Bond Number can be used to determine the conditions under which gravity should be considered when simulating DNAPL invasion in a macroscopically horizontal fracture. Finally, a set of equations defining the minimum and maximum absolute percentage difference between gravity‐included and gravity‐neglected simulations is presented based on the fracture and DNAPL characteristics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".