An Evaluation of NAPL Wettability in 2-D Visualization Experiments
Bibliographic record
Abstract
The presence of light non-aqueous phase liquid (LNAPL) in the subsurface constitutes a long-term source of pollution for groundwater. Hence understand the movement of NAPL in the subsurface is essential in order to design effective remediation technology. Their movement in the subsurface is so complex that researchers have been using different media, different NAPLs, and conducted experiments in 1-D column, 2-D tank and 3-D tank in order to study the phenomenon. To solve the problem of limitation in flow boundaries in 1-D researchers have used two dimensional (2-D) tanks made of glass and plexiglass. However there have been some questions concerning the possible impact of NAPL wettability on materials use to construct the tank. This wettability may influence the flow of fluids at the visual interface. A representative LNAPL (dodecane) was released in the vadose zone of an Ottawa sand. Two external constant head reservoirs were used to maintain a constant water table in the tank. Time-series digital images of plume were used to analyze the geometry and position of the plume in the tanks regarding to the water table. Then relative geometry of the LNAPL was compared to Pantazidou and Sitar's equation. In both the sand and the glass tank, water is the wetting fluid, whereas dodecane (LNAPL) is the non-wetting fluid. However, on the plexiglass the LNAPL is the wetting fluid. As a result 40% and 70% difference were observed between glass and plexiglass tanks regarding the length and the thickness, and the area was twice the size of the plume in the glass tank. This demonstrates that the tank material is an important parameter to be considered on flow visualization of NAPL. Moreover, the influence of ethanol on dodecane was tested on a 2-D tank in glass. The results showed that the presence of ethanol appears to influence on the size and position of the plume in the subsurface.
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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 | 0.001 | 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) | 0.001 | 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 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".