Complementary use of analytical equations and numerical models for composite liner designs
Bibliographic record
Abstract
The finite element (FE) method is used to identify limitations and extend the applicability of an analytical solution for leakage through holed wrinkles in composite liners. The limiting assumption in the analytical model is relaxed based on FE results, providing a means of obtaining good agreement through the two methods over a wide spectrum of cases. Despite the flexibility of the FE, the analytical model has the advantage of readily accommodating a wide range of dimensions and material properties that, even with a very large mesh, may be difficult to analyze using FE. In addition, the analytical equation is well-suited for Monte Carlo simulation. This is illustrated by the use of the modified analytical equation and the Monte Carlo simulations to compare the performance of two types of composite liners currently approved for use in Chinese landfills with a generic design in Canada and other parts of the world. It is shown that even with a relatively small number of holed wrinkles leakage through the geomembrane composite liner can differ by up to two orders of magnitude between composite liners considered to be equivalent. The paper also notes the need to revise regulations to reflect the evolution of knowledge.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".