Experimental and numerical investigations into hydraulic behaviour of coir fibre drain
Bibliographic record
Abstract
Over many decades, natural fibre bundles have been widely used for drainage and filtration applications because of their favourable hydraulic conductivity and abundance in Asian countries. In recent times, natural (biodegradable) coir and jute drains, which are environmentally friendly, have been considered in lieu of conventional geosynthetic wick drains for soft clay consolidation in Australian coastal regions. However, there is a lack of a computational framework to predict the hydraulic behaviour of fibre drains on the basis of micromechanical (fabric) characteristics. Employing computational fluid dynamics (CFD) coupled with the discrete element method (DEM) to model the hydraulic behaviour of fibrous materials has shown promise in an earlier 2016 study by Nguyen and Indraratna, which considered an idealized parallel arrangement of fibres for simplicity. This paper aims to broaden the application of the coupled CFD–DEM technique to real fibres (coconut coir) considering both nontwisted and twisted fibre bundles that have more complex porous structure. The hydraulic conductivity determined from the numerical approach is validated with the experimental results, and also compared with the analytical prediction based on the conventional Kozeny–Carmen (KC) approach. The current study shows that the CFD–DEM technique can capture well the fluid flow characteristics of a nonuniform fibrous structure, including dense twisted coir bundles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".