Predicting Leak Rate Through Valve Stem Packing in Nuclear Applications
Bibliographic record
Abstract
Leaking valves have forced shutdown in many nuclear power plants. The myth of zero leakage or adequate sealing must give way to more realistic maximum leak rate criterion in design of nuclear bolted flange joints and valve packed stuffing boxes. It is well established that the predicting leakage in these pressure vessel components is a major engineering challenge to designers. This is particularly true in nuclear valves due to different working conditions and material variations. The prediction of the leak rate through packing rings is not a straightforward task to achieve. This work presents a study on the ability of microchannel flow models to predict leak rates through packing rings made of flexible graphite. A methodology based on experimental characterization of packing material porosity parameters is developed to predict leak rates at different compression stress levels. Three different models are compared to predict leakage; 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 and the lattice model is based on porous media of packing rings as packing bed (Dp). The flow porosity parameters (N, R) of the microchannels assumed to simulate the leak paths present in the packing are obtained experimentally. The predicted leak rates from different gases (He, N2, and Ar) are compared to those measured experimentally in which the set of packing rings is mainly subjected to different gland stresses and pressures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".