Leakage Estimate in Nonuniformly Compressed Packing Rings
Bibliographic record
Abstract
Abstract Characterizing the permeation performance of nanoporous material is an initial step toward predicting microflows and achieving acceptable designs in sealing and filtration applications. This study deals with analytical, numerical, and experimental studies of gaseous leaks through soft packing materials subjected to nonuniform axial compression in valve stuffing boxes. A new analytical model that accurately predicts gaseous leak rates through nanoporous packing materials assumed made of capillaries having an exponentially varying section. Based on Navier–Stokes equations with the first-order velocity slip condition for tapered cylinder capillaries, the analytical model is used to estimate gas flow through soft packing materials. In addition, computational fluid dynamic modeling using cfx software is used to test its capacity to estimate the permeation of compression packing ring materials assuming the fluid flow to follow Darcy's law. Helium gas is used as a reference gas in the experiments to characterize the porosity parameters. The analytical and cfx numerical leak predictions are compared to leak rates measured experimentally using different gas types (helium, nitrogen, air, and argon) at different pressures and gland stresses. The analytical and numerical models account for the porosity change with the stem axial distance because the packing ring set is subjected to an exponentially varying radial compression. The predictions from analytical model are in close agreement with the cfx model and in better agreement with experimental measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".