Hydraulic behaviour of parallel fibres under longitudinal flow: a numerical treatment
Bibliographic record
Abstract
Modelling fluid flow through fibrous porous materials has gained increasing attention from industry and research communities. Analytical and numerical methods are commonly used to predict the hydraulic characteristics of fibrous material during fluid flow, although to date most techniques have been conducted using the same assumption that the geometric features of fibres remain unchanged. In other words, the mutual interaction between fibre elements and fluid is ignored, which undermines the actual working condition of fibres. This paper therefore presents a potential numerical approach that is capable of capturing the behaviour of a fluid–solid system. Individual fibres are simulated by the discrete element method (DEM) coupled with the concept of computational fluid dynamics (CFD), whereby the information contained in each phase is constantly exchanged and updated with other phases. In comparison with conventional solutions, including the Kozeny–Carman (K–C) fluid flow principle and other valid studies, the results show an acceptable agreement in predicting the hydraulic conductivity of a fibrous system. Subjected to laminar longitudinal flow, fibre motion is also evaluated with respect to varying bond stiffness and flow velocity. The study indicates the potential of the proposed technique in modelling drainage and filtration that is based on the hydraulic behaviour of fibrous porous geomaterials.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".