Leak Rates of Gasses Through Packing Seals With Different Analytical Approaches
Bibliographic record
Abstract
Predicting leakage in packed stuffing boxes is a major engineering challenge to designers and end users. Due to the different working conditions and material products, the determination of the flow regime present in packing rings is not a straightforward task to predict. This paper presents a study on the ability of micro channel flow models to predict leak rates through packing rings made of soft materials such as graphite. A methodology based on the experimental characterization of the porosity parameters is developed to predict leak rates at different compression stress levels. Three different models are compared to predicate the leakage, where the diffusive and second order flow models are derived from Naiver-Stokes equations and incorporate the boundary conditions of an intermediate flow regime to cover the wide range of leak rate levels. The lattice model is based on porous media of packing rings as packing bed (Dp). The flow porosity parameters (Rc,Dp) of the micro channels assumed to simulate the leak paths present in the packing are obtained experimentally. The predicted leak rates from different gasses (He, N2, Ar) are compared to those measured experimentally, in which the set of packing rings is mainly subjected to different gland stresses and pressures.
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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".