Permeability Measurement of Granular Porous Materials by a Modified Falling-Head Method
Bibliographic record
Abstract
Liquid flows through granular material are common phenomena in different engineering fields. Under certain conditions, this type of flow can be described by Darcy’s law, which involves the permeability of the porous medium. Experimental characterization of this parameter is then of importance to many practical applications. The falling-head permeability test is regarded as one of the most commonly used methods for that purpose. The required manipulations are easy and rapid, which makes it preferable, especially for field tests. However, due to practical difficulties of carrying out such measurements, it is only applicable to porous materials with permeability values lower than 10−10 m2. To enlarge the test range while keeping its advantages, a modified test procedure is proposed here to measure saturated permeability values two orders of magnitude larger, namely around 10−8 m2. Tests were performed on granular beds containing single-diameter and multiple-diameter beads. Experimental results showed that the saturated permeability can be accurately predicted by the revisited Ergun’s equation with its first empirical constant equal to 180. Two important factors must be considered when performing these tests. The first one is the data-processing method to compute the permeability from experimental data: both the gravity and pressure drop of the setup must be taken into account. The second one is the selection of test fluid. By comparing water and silicone oil, it was shown that the viscosity should be adapted depending on the permeability of the sample to ensure consistent and repeatable results.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".