Development of Multiphysics Models for the Study of Airflow and Thermal Effects During the Use of Filtering Facepiece Respirators
Bibliographic record
Abstract
To provide good protection, a Filtering Facepice Respirator (FFR) must be properly adjusted to ensure a satisfactory seal against the external working environment. None of the tests currently in use can accurately locate potential air leaks and determine a fit factor. T he aim o f t he project is to develop an analysis station for assessing these quantities using infrared imaging methods. The development of a multiphysics model of the different situation will become a tool for better understanding phenomena by simulating situations that are difficult t o verify experimentally. This paper proposes the development of multiple multiphysics models using COMSOL Multiphysics® Software to simulate the thermal effects of air leaks during the use of FFR. The construction of various geometries, transitioning from a simple 2D model to a 3D model closely resembling real-life situations, is detailed as follows. The implementation of diverse physics and meshing techniques related to the problem, including thermal dynamics and fluid mechanics h as been thoroughly studied. A pivotal aspect considered in this context is the respiratory cycle. The preliminary results show that 1) the impact of humidity on heat exchange is low and negligible to a first approximation, 2) the variation of the flow rate at the leak is linked to its size, and, in particular, 3) the temperature evolution in zones proximate to the leaks as calculated by the model are compared with the experimental results obtained with an infrared camera. As part of a broader project, these models will be instrumental in comparing and validating forthcoming experimental results. The simulation results obtained will also be used to feed a database that will be used in artificial intelligence model asp art o f t he development of the analysis station.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".