Steady state mass transfer from single‐component dense nonaqueous phase liquids in uniform flow fields
Bibliographic record
Abstract
In recent years it has become increasingly clear that most remedial technologies fail to completely remove dense nonaqueous phase liquid (DNAPL) from subsurface source zones. Recognition of this limitation leads to the question of what benefit can be achieved through partial removal of DNAPL. To address this issue, a mathematical technique referred to as the multiple analytical source superposition technique (MASST) has been developed. MASST is based on a conceptualization of a DNAPL source zone as a grouping of discrete subzones containing DNAPL (e.g., fingers and/or pools) separated by portions of the aquifer that are entirely free of DNAPL. Using analytical techniques, spatial superposition of responses to multiple sources is used to estimate aqueous mass transfer rates from individual subzones. This procedure accounts for multiple DNAPL subzones with different volumes, geometries, and locations within an overall source zone that is otherwise free of the nonaqueous liquid. The mass transfer rate from a particular subzone is affected by mass transfer from all other subzones in the vicinity. Groundwater flow is assumed to be uniform, and transport processes are considered to be at a steady state. Comparison of MASST results with exact analytical solutions and laboratory data confirms the validity of MASST. Sensitivity analyses indicate that source‐zone architecture is a primary factor governing bulk mass transfer and source longevity. Analysis of rate‐limited mass transfer within DNAPL subzones and advective‐dispersive transport about DNAPL subzones indicates that advective‐dispersive transport is the primary factor controlling mass transfer rates. Finally, results indicate that removal of the vast majority of the DNAPL will likely be necessary to achieve significant near‐term improvements in groundwater quality.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".