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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".