Predicting Gasket Leak Rates Using a Laminar-Molecular Flow Model
Bibliographic record
Abstract
The tightness characterization of gaskets used in static seal applications, such as bolted flanged connections, is achieved by performing leakage tests with a single fluid, usually a gas like helium. Attempts made in the past to predict gasket leakage with other gases had limited success unless the leak flow regime through the gasket was predominately laminar, which is not the case with most of the gaskets. In this work, a new gasket leak flow model that combines both molecular and laminar flow regimes is developed to predict the gasket leak rate under different pressures and with different gases. The Laminar-Molecular Flow (LMF) model is first constructed around a reference pressure for which the fraction of the total leakage that occurs through laminar flow channels is established. This fraction is computed using a simple leakage test performed with one gas and at least two different pressures. The model is then tested against experimental leak data obtained from two different gaskets and four gases and is shown to produce accurate predictions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".