Prediction of Leak Rates Through Porous Materials Using Analytical and Numerical Approaches
Bibliographic record
Abstract
Characterizing the permeation performance of nano-porous material is an initial step towards predicting micro-flows and achieving acceptable designs in sealing and filtration applications. The present study deals with analytical, numerical, and experimental studies of gaseous leaks through soft packing materials. The paper presents a new analytical model to accurately predict and correlate gaseous leak rates through nano-porous materials. The analytical prediction is done with a model of fluid flow through capillaries of an exponentially varying section. Based on Navier-Stokes equations with different flow regimes, the analytical model is used to predict gaseous flow rates through soft packing materials. In addition, for comparison, computational fluid dynamic modelling using CFX software is used to estimate the flow rate of compression packing ring materials assuming the fluid flow to follow Darcy’s law. Helium gas is used as a reference gas to characterize the porosity parameters. The analytical and CFX numerical leak predictions are compared to leak rates measured experimentally using different gas types (Helium, Nitrogen, Air, and Argon) at different pressures and gland stresses. The packing material is subjected to different compression stress levels in order to change its porosity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".