Evaluating and applying contaminant transport models to groundwater systems
Bibliographic record
Abstract
This thesis examines the use of random walk techniques to model the transport of a contaminant in groundwater. These techniques involve the distribution of a plume of contaminant into a discrete number of particles. These particles are then individually subjected to advective and dispersive forces and their progress though the model domain tracked over time. In general, pollution of groundwater is characterised by: 1) being difficult to detect, 2) being complicated and expensive to investigate and monitor, and 3) being expensive to clean up. These factors make the modelling of groundwater contamination an important area of investigation. This thesis presents the principles of groundwater flow and contaminant transport, along with their governing equations (namely, the groundwater flow equation and the advection dispersion equation); methods for the solution of the advection dispersion equation are discussed. These methods include analytic, finite difference and random walk techniques. Three random walk techniques are presented and compared with the analytic solutions for the following cases: 1) one dimensional dispersion 2) one dimensional advection dispersion 3) two dimensional dispersion 4) two dimensional advection dispersion Results of the comparisons have showed that all three random walk schemes presented produce computed results which are consistent with the analytic solution in each of the cases considered. Two finite difference schemes are presented and applied to the case of two dimensional advection dispersion. Through doing so, the problem of numerical diffusion has been highlighted. Random walk techniques have been applied to two physical problems. In the first case, a model has been developed to simulate the movement of a plume of chloride in an aquifer in the province of Saskatchewan, Canada. Results are compared for each of the three random walk schemes, namely time histories and breakthrough curves which plot the concentration of particles at a location in space over the time period modelled. All three random walk techniques have produced results that are very similar, with each modelling the movement of the plume acceptably. The second model uses data from The South Australian Department of Mines and Energy to simulate the movement of salt in the groundwater in a vine growing region near Naracoorte, South Australia. This model produces results which are consistent with available measured data.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".