Evaluating Friction and Inertial Losses From Slug Tests Conducted in a Multilevel System
Bibliographic record
Abstract
Abstract Engineered multilevel systems (MLS) are one of the few viable options for collecting spatial and temporal datasets in 3‐D complex groundwater flow systems. However, these monitoring systems present challenges for hydraulic testing due to small port tubing diameters. In this study, an equipment was developed to enable a pneumatic slug testing of a G 360 MLS, fitted with 1/2‐inch ID open tubes that extend from the monitoring ports to the surface. Of the eight ports tested, four exhibited overdamped slug test responses, while underdamped responses were observed in the remaining four ports. It is expected that friction in the small diameter tubing is not negligible, and additional flow constrictions are introduced when installing a transducer in the tubing, effectively changing the open tube geometry to an annulus around the transducer body and around the transducer cable. Steady flow models for annular flow, widely available in the literature, were used to assess tube friction for the overdamped tests. However, for underdamped tests that oscillate quickly, a mathematical solution for unsteady oscillatory flow through an annulus was derived. The results of this study show that it is important to account for frictional and inertial losses to obtain good transmissivity (T) estimates from slug tests conducted in small diameter tubing. Assuming steady flow through small annuli will not introduce an appreciable error when calculating the water level; however, if steady flow through the open tube below the transducer is assumed when calculating the formation head, T values can be underestimated by as much as 80%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".