An Experimental-Numerical Procedure for Stuffing Box Packing Characterization and Leak Tests
Bibliographic record
Abstract
The sealing of valve stems is ensured by the traditional systems of packed stuffing boxes. The performance of this type of sealing system, which is also used in rotating equipment, is dependent on the radial contact pressures generated by the packing axial compression. The mechanical behavior of a packing seal is characterized by the transmission ratio of the radial stress over the axial stress known as the lateral pressure coefficient, which is one of the required parameters used to select packing seals. However, the modeling of the packed stuffing box requires the knowledge of other packing seal mechanical characteristics such as compression modulus and Poisson’s ratio. In this paper, the mechanical characteristics of packing seals are obtained using a hybrid experimental-numerical procedure. The experimental study is carried out on an instrumented stuffing box packing test bench. The tests results of the experiments are coupled to the ones obtained by a finite element simulation of the test rig to determine the packing seal mechanical characteristics. Two packing types are used: one based on Teflon and the other one based on flexible graphite. In addition, leak rates are measured for different axial compressive stresses and gas pressures in order to estimate the tightness performance of such seals.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".