Effects of microstructure on flow properties of fibrous porous media at moderate Reynolds number
Bibliographic record
Abstract
In this study, effects of microstructure on the viscous permeability and Forchheimer coefficient of monodispersed fibers are investigated. The porous material is represented by a unit cell which is assumed to be repeated throughout the medium. Based on the orientation of the fibers in the space, fibrous media are divided into three categories: one-, two-, and three-directional (1D, 2D, and 3D) structures. Parallel and transverse flow through square arrangements of 1D fibers, simple 2D mats, and 3D simple cubic structures are solved numerically over a wide range of porosity (0.35 < ε < 0.95) and Reynolds number (0.01 < Re < 200). The results are used to calculate the permeability and the inertial coefficient of the considered geometries. An experimental study is performed; the flow coefficients of three different ordered tube banks in the moderate range of Reynolds number (0.001 < Re < 15) are determined. The numerical results are successfully compared with the present and the existing experimental data in the literature. The results suggest that the permeability and Forchheimer coefficient are functions of porosity and fiber orientation. A comparison of the experimental and numerical results with the Ergun equation reveals that this equation is not suitable for highly porous materials. As such, accurate correlations are proposed for determining the Forchheimer coefficient in fibrous porous media.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".