ICONE23-2168 ON THE MODELING OF GAS FLOW THROUGH POROUS COMPRESSION PACKINGS USED IN VALVE STUFFING-BOXES
Bibliographic record
Abstract
Predicting leak rate through porous compression packing rings is a significant challenge for the design of packed stuffing boxes. Although few studies have been conducted to predict the leak rate through these seals, there is no comprehensive standard procedure to be used to design compression packings for a maximum tolerated leak for a given application. With the ubiquitous use of the yarned packing rings and the strict regulations on fugitive emissions and the new environment protection laws quantification of leak rate through yarned stuffing boxes becomes more than necessary and a tightness criteria based design procedure must be developed. In this study a new approach to predict leak rate through compression packing rings has been developed. It is based on Darcy's model to which Klinkenberg slip effect is incorporated. The predicted leak rates are compared to those measured experimentally using two different graphite-based packing rings subjected to different compression levels and pressures. A good agreement is found between the predicted and the measured leak rates which illustrates the validity of the developed model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".