A simple numerical method to simulate the flow through filter media: Investigation of different fibre allocation algorithms
Bibliographic record
Abstract
Abstract Flow patterns and pressure‐drop through three fine‐fibre air filter media were simulated using the lattice Boltzmann method. The geometry for the flow domain was two‐dimensional, with fibres having truncated log‐normal diameter distributions matching SEM photograph measurements on the actual media. The influence of the strategy in positioning the fibres inside the computational domain on the pressure drop of the filter media was investigated. Two different schemes were proposed to position the fibres into a cross section of the filter medium: a random distribution algorithm and the Mitchell's best candidate algorithm. Furthermore, no‐slip and free‐slip boundary conditions were tested for the fluid‐fibre interaction. The comparison between numerical and experimental data shows that the random allocation of fibres better predicted the filter media behaviour. Simulated data suggest that the free‐slip boundary condition must be used when studying the interaction of the fluid with small fibres similar to the size of the fibres used in this study. This study allowed the development of a simple strategy to estimate the pressure drop of a filter medium by having little information of its physical structure, such as solid fraction and diameter distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".