Liquid Leak Predictions in Micro and Nano-Porous Gaskets
Bibliographic record
Abstract
In recent years, 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. A new method based on a slip flow model to predict liquid flow through nano-porous gaskets is presented. A recent study [1] had shown that the leakage prediction based on the porosity parameter approach was successful in predicting gaseous leaks and an extrapolation of the latter to liquid leaks is the purpose of this study. In the present article, an analytical-computational methodology based on the number and pore size to predict liquid nanoflow in the slip flow regime through gaskets is presented. The formulation is based on the Navier-Stokes equations associated to slip boundary condition at the wall. The mass leak rates through a gasket considered as a porous media under variable experimentally conditions of (fluid media, pressure, and gasket stress) were conducted on a test bench. Gaseous and liquid leaks are measured and comparisons are made with the analytical predictions.
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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".