An Investigation of the Pressure Pulsing Reagent Delivery Approach
Bibliographic record
Abstract
Abstract Pressure pulsing technology is an innovative method that has been developed with the aim of overcoming preferred flow paths associated with remediation techniques that rely on the injection of reagents. Numerical and field experiments were conducted to assess how pressure pulsing affects groundwater flow and solute transport during reagent injection. A series of field experiments were performed at two field sites where a monitoring network designed to capture the breakthrough of solutes delivered from an injection well was installed. Pressure pulsing and conventional injection methods were used at each site. One site was comprised of fine sand with low heterogeneity, while the other was moderately heterogeneous with discrete layers varying from fine sand to silt. The data suggest that breakthrough was more uniform for the pressure pulsing injections; however, this difference was minor and complicated by sorption of some of the tracers employed. The groundwater flow and solute transport modeling exercise simulated the rapid boundary pressure modulation that occurs in association with pressure pulsing. Two‐dimensional ( 2D ) simulations revealed that repeated sudden onset of injection cessation produces brief periods of gradient reversal and the development of a mixing zone near the injection well. The spatial extents of this mixing zone were found to be highly dependent upon the hydraulic diffusivity of the medium, with medium heterogeneity and pulsing frequency playing secondary roles. Three‐dimensional ( 3D ) numerical simulations were used to benchmark the observations from one of the field sites. The results from the modeling effort showed that solute breakthrough from a pressure pulsing injection is more dispersed relative to a conventional injection as a result of the mixing zone phenomenon; however, we were unable to directly observe this mixing zone using the instrumentation deployed at the two field sites.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".