Liquid Leak Predictions in Micro- and Nanoporous Gaskets
Bibliographic record
Abstract
In recent years, quite few experimental and theoretical studies have been conducted to predict gas leak rate through gaskets. However, a very limited work is done on liquid leak rates through gaskets. The slip flow model is used to predict liquid flow through porous gaskets based on measurements of gas flow at different pressures. In fact, an extrapolation of the porosity parameter approach (Grine, L., and Bouzid, A., 2009, “Correlation of Gaseous Mass Leak Rates Through Micro and Nano-Porous Gaskets,” ASME Paper No. PVP2009-77205) used to correlate leak rates between different gases is used to predict liquid leak rates. In the present article, an analytical-computational methodology based on the number and pore size to predict liquid micro- and nanoflows in the slip flow regime through gaskets is presented. The formulation is based on the Navier–Stokes equations associated with slip boundary condition at the wall. The mass leak rates through a gasket considered as a porous media under various experimental conditions of fluid media, pressure, and gasket stress were conducted on a special gasket test rig. Gaseous and liquid leaks are measured and comparisons with the analytical predictions are made.
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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 | 0.000 | 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".