Modelling the stochastic nature of porosity in a respirator canister using computational fluid dynamics
Bibliographic record
Abstract
Abstract A model has been developed to represent the stochastic nature of the activated carbon bed within a generic chemical biological radiological and nuclear (CBRN) respirator canister. The porous region is subdivided into discrete sections which are assigned a porosity based on their radial position according to a longitudinally‐averaged porosity model, and then perturbed by some amount according to a Gaussian distribution. The porosity model was used in Reynolds‐averaged Navier‐Stokes (RANS) simulations in order to assess the impacts that the choice of section size and the porosity variation would have on the pressure drop and residence time distribution. It was shown for small section sizes that increasing porosity variation would increase pressure drop and minimum residence time in the carbon bed, while decreasing the average residence time. As the section sizes became larger the reverse trend was seen as a greater extent of flow channelling throughout the bed became apparent. For the given domain size, there was an upper limit to the section size, beyond which statistical convergence could not be guaranteed. It was also shown that for section sizes close to the particle diameter, the results would depend only on the ratio of section size to porosity variation, reducing the porosity model to a single parameter. A few selected cases were simulated at higher flow rates, where the previously mentioned trends were seen to persist.
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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 | 0.001 |
| 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".