On the Use of Gas Flow Models to Predict Leak Rates Through Sheet Gasket Materials
Bibliographic record
Abstract
The prediction of leak rate through porous gaskets for different gases based on test conducted on a reference gas can prevent bolted joint leakage failure and save the industry a lot of money. This work gives a basic comparison between different gas flow models that can be used to predict leak rates through porous gasket materials. The ability of a model to predict the leak rate at the micro and nano levels in tight gaskets relies on its capacity to incorporate different flow regimes that can be present under the different working conditions. Four models based on Navier-Stokes equations and incorporate the boundary conditions of the appropriate flow regime considered. The first and second order slip, diffusivity and molecular flow models are used to predict and correlate leak rates of gases namely helium, nitrogen, SF6, methane, argon and air passing through three frequently used nanoporous gasket materials which are flexible graphite, PTFE and compressed fiber. The methodology is based on the determination experimentally of the porosity parameter (N and R) of the micro channels assumed to simulate the leak paths present in the gasket using helium as the reference gas. The predicted leak rates of different gases at the different stresses and pressure levels are confronted to the results obtained experimentally by measurements of leak rates using pressure rise and mass spectrometry techniques. The results show that the predictions depend on the type of flow regime that predominates. Nevertheless the second order slip model is the one that gives better agreements with the measured leaks in all cases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".