Numerical modeling of rising-head permeability tests in monitoring wells after lowering the water level down to the screen
Bibliographic record
Abstract
To begin a rising-head permeability test in a monitoring well (MW), the water level is lowered in the pipe. If it is lowered down to the screen, the recovery graph may differ from the theoretical straight line, making it difficult to assess the mean field hydraulic conductivity. A numerical analysis (finite element method) of this type of test, considering the complete equations for saturated and unsaturated flow, is presented. The numerically obtained graphs are similar to those of real field tests. In an aquitard, when the filter pack is made of fine sand, which retains water by capillarity, the screen dewatering influence is hardly visible in the velocity graph and is undetected in the usual semilog graph. In an aquitard, when the filter pack cannot retain water by capillarity during dewatering, the semilog graph presents two straight-line portions. The velocity graph, a representation of the conservation equation, helps to distinguish the early time interval, when the groundwater fills the screen and the filter pack, and the later interval, when it fills only the pipe. The later portion of the graph must be used to calculate the hydraulic conductivity. In an unconfined aquifer, when there is no filter pack, dewatering down to the screen by pumping significantly lowers the water table around the MW. The usual semilog graph appears as a set of two straight lines. The velocity graph indicates that all calculations must consider a piezometric level that is lower than that measured before dewatering. In all cases, the velocity graph shows clearly what happened during the numerically simulated tests. The more complex case of an MW installed with a filter pack in an unconfined aquifer and tested using a mechanical slug was not numerically examined in this paper.Key words: hydraulic conductivity, rising head, monitoring well, numerical analysis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".