Prediction of Leak Rates in Porous Braided Packing Rings
Bibliographic record
Abstract
The prediction of leakage is one of the most challenging tasks when designing bolted flanged connections and valves. Failure of these pressure vessel components can cause shutdowns but also accidents, loss of revenue and environmental damages. With the strict regulations on fugitive emissions and environmental protection laws new tightness-based standards and design methods are being adopted to improve the sealing performance of bolted joints and valves. However, there is a practical interest in using a reliable correlation that could predict leak rates of one fluid on the basis of tests carried out with another on compressed packings. The paper presents an innovative approach to accurately predict and correlate leak rates in porous braided packing rings. The approach is based onDarcy-Klinkenberg to which a modified effective diffusion term is added to the equation. Experimental measured gas flow rates were performed on a setof graphite based compression packing rings with a large range of leak rates under isothermal steady conditions. Leakage from three different gases namely Helium, Nitrogen and Argon were used to validate the developed correlation. In the presence of the statistical properties of porous packings, the leak rates for different gases can be predicted with reasonable accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".