Modeling of Aerosol Deposition with Interface Devices
Bibliographic record
Abstract
Various approaches can be used to mathematically model the performance of different masks, mouthpieces, and aerosol delivery devices. The sophistication of such models can vary widely, from the use of simple algebraic empirical correlations to advanced computational fluid dynamics simulations. Bench-top testing is also often used to model aspects of devices, since it is difficult to capture certain aspects of device behavior with mathematical models. These various approaches to modeling differ in their limitations. Empirical correlations exist for predicting the effects of varying mouthpiece diameter and mouth-throat dimensions on extrathoracic losses, but are restricted to stable, nonballistic aerosols in certain flow rate ranges. Computational fluid dynamics (CFD) simulations that solve the Reynolds-averaged Navier-Stokes (RANS) equations typically require near-wall turbulence corrections in order to adequately model mouth-throat deposition, while Large Eddy Simulation (LES) removes this deficiency. Bench-top models that use replicas of the extrathoracic airways vary in their accuracy and generality in replicating the filtering properties of these airways. Choosing and using these various modeling approaches for evaluating patient-device interfaces requires knowledge of their merits and pitfalls, a brief discussion of which is given here.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".