Analytical modelling of the permeability of filter media exhibiting a bimodal fibre diameter distribution—Beyond empirical models
Bibliographic record
Abstract
Abstract The prediction of the permeability of bimodal fibrous media dedicated to air filtration, that is, highly porous media, is addressed. Two types of models, using similar input parameters (mean porosity and mean fibre diameter), are compared: several analytical models and the empirical models still commonly used by the air filtration community. It is demonstrated than even if every model under‐predicts the media permeability, the isotropic versions of the analytical representative unit cell (RUC) model and Tomadakis and Robertson model yield significantly better predictions than the empirical models. The additional improvement in the analytical model predictions, by including the bimodal fibre diameter distribution as a unimodal equivalent diameter, is furthermore outlined. This model comparison is made possible due to new accurate experimental data, obtained from the characterization of the structural properties of a commercial bimodal fibrous medium. The fibre diameter distribution and mean medium porosity are determined from SEM analyses and mercury porosimetry, respectively. The anisotropic/isotropic and homogeneous/heterogeneous nature of the medium is evaluated based on X‐ray micro‐tomography data. Finally, to go further than the prediction of one permeability value based on mean media properties, permeability mappings are generated based on the porosity mappings and the adapted RUC permeability model.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".