Impact of multilayering on the filtration performance of clean air filter media
Bibliographic record
Abstract
Abstract Fibrous filter media are commonly used to remove airborne particles that are harmful to human health and the environment. Although filter media are often multilayered for various reasons, no systematic study of the impact of multilayering on filter media performance has been reported. In this paper, direct numerical simulations with the lattice Boltzmann method are used in order to shed light on the impact of multilayering on the performance of clean bimodal fibrous filter media in a Stokes flow regime. Virtual model clean filter media with up to eight layers and various fibre formulations are compared in terms of permeability or pressure drop, capture efficiency, and quality factor. A careful analysis of the results revealed that multilayering had no statistically significant impact on the performance of the clean filter media. At best, the impact of multilayering was similar to that of the inherent variability of such random structures. Fibre formulation was found to be a more efficient way of improving the performance of the filter media. Placing interlayered air gaps between fibrous layers also slightly improved the quality factor by facilitating the flow at the interfaces of the fibrous layers. These findings will guide future studies on the performance of multilayered filters with more complex flow conditions, such as those encountered with inertial or nanofibre‐made filter media and with the fouling of filter media.
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 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".