A Lattice Boltzmann approach for predicting the capture efficiency of random fibrous media
Bibliographic record
Abstract
Abstract We study the propagation of submicron airborne particles through random fibre networks such as paper. In our approach, we first construct a three‐dimensional model of the network and then use a Lattice Boltzmann method to obtain the flow of air through that structure. We finally calculate the trajectories of airborne particles and determine the fraction of these particles that impinge on fibres in the network. The combined approach is used to obtain pressure drop and mechanical filtration efficiency curves for a variety of structures. Our results show that, at fixed pressure drop and flow rate, a filter with a high basis weight and porosity will perform better than one made from fewer fibres that are more densely packed, at least in the range of porosities considered. For filters with a bimodal fibre size distribution, we find that the minimum in the efficiency curve becomes sharper and moves to smaller particle sizes as the mean fibre diameter of the mixture decreases, as expected from single‐fibre theory. The efficiency of capture by diffusion and interception exhibits a weaker dependence on surface area mean fibre diameter than that predicted by theory, in agreement with the observations of Brown and Thorpe. Copyright © 2010 Curtin University of Technology and John Wiley & Sons, Ltd.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".