Theoretical study of pressure distortions in a mechanical seal
Bibliographic record
Abstract
Nowadays, in various industries such as aerospace, atomic energy, chemical industries, refineries, petrochemical industries, and others, there is a need to use high-pressure mechanisms to enable and complete related processes. The increase in pressure occurs within a cylindrical chamber, and as a result, such an area must be sealed against fluid leakage from the inside to the outside. As the pressure rises, numerous problems related to fluid leakage arise because, as the process progresses, the pressure difference between the inside and outside of the system increases, consequently increasing the fluid's tendency to leak. The container becomes like a bomb where the slightest disturbance in the sealing system can cause leakage, even explosion, and irreparable damage. Therefore, for carrying out such processes (high-pressure processes), safe sealing is one of the most essential needs. This research focuses on designing a high-pressure seal based on the Bridgman design, in such a way that the sealing operation is performed reliably using the fluid pressure and its increase by the sealing assembly. To evaluate the performance of the seal, simulation using Abaqus software is utilized. Various sealing rings are examined, and ultimately, the ring that can not only create sufficient pressure (more than the fluid pressure) but also provide a more uniform pressure distribution (to prevent seal damage) is selected. In this research, PA6, UHMWPE-glass, UHMWPE-ceramic, NBR, and silicone are used for the high-pressure seal. PA6, UHMWPE-glass, and UHMWPE-ceramic exhibit elasto-plastic and time-dependent behavior. Therefore, for their simulation, elastic and viscoelastic models are used. NBR and silicone exhibit hyperelastic and time-dependent behavior, and for their simulation, hyperelastic and viscoelastic models are used.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".