2D modelling of clogging in landfill leachate collection systems
Bibliographic record
Abstract
A 2D model for predicting clogging of a landfill leachate collection system and subsequent leachate surface position (mounding) is described. A transient finite element fluid flow model is combined with a reactive, multiple-species finite element transport model. The transport model considers biological growth and biodegradation, precipitation, and particle attachment and detachment. It uses a geometrical relationship to establish porosity from the computed thickness of the accumulated clog matter and a relationship between the porosity and hydraulic conductivity of elements in the system. The model represents the flow path within the drainage layer in profile. An iterative method is used to solve for the new hydraulic heads, surface and internal nodal positions, and redistributed clog properties (clog quantity, porosity, hydraulic conductivity) for each element and for each time step. The porosity (and consequently hydraulic conductivity) of the media can therefore change spatially and temporally. The mesh is regenerated automatically each time step (including the addition or subtraction of nodes) taking into account allowable element aspect ratios, the interfaces between differing hydrostratigraphic layers, and static point sources and openings. An integrated alternate solution for very thin mounds is included. The application of the model is demonstrated using a hypothetical field case.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".