A Study on the Tightness Parameter Used in the Evaluation of the PVRC Gasket Constants
Bibliographic record
Abstract
Abstract The value of the tightness exponent used in the leakage-to-pressure relationship has faced significant criticism. It was developed during a time when fiber gaskets were the standard, prior to the introduction of more advanced gasket materials like flexible graphite and PTFE in the early 1990s which are much tighter and require less load to seal. To account for the diverse flow regimes that may occur in modern gasket materials, a reevaluation of the pressure-tightness relationship is necessary. This is achieved by an evaluation of the slope of the leak rates plotted against a wide range of fluid pressures and repeated at different gasket stress levels. Therefore, a comprehensive study involving leak tests conducted on various gasket materials and fluid media, subjected to different internal pressures and contact stresses, has been conducted to adjust this correlation. The findings indicate that a tightness exponent of ¾, rather than the current ½, more accurately reflects real-world conditions. For a comparison purpose, the gasket constants calculated with tightness parameter based on both exponents were used to predict leak rates. It was found that the revised constant produces more accurate predictions, better aligning with measured leak rates. The presence of the different types of flows including porous and surface leaks, laminar and molecular that can be present individually or combined in any given gasket are better described. The required gasket contact stresses to achieve target leak rates were found to be lower. This version enhances readability, ensures technical clarity, and improves the overall 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".