Characterisation of Polytetrafluoroethylene and Fiber Based Gaskets Under Creep and Thermal Ratcheting
Bibliographic record
Abstract
The objective of this research work is to typify PolyTetraFluoroEthylene (PTFE) and fiber based gaskets. Recurrent employment of Teflon based gaskets as asbestos gasket replacement in certain applications is due to their effective leak tightness and excellent chemical resistance. The research investigates the effect of cyclic and determinant temperature on the phenomenon of creep and thermal ratcheting under compressive load. The tests are performed at 4000 and 6000 psi compressive stress on virgin PTFE (vPTFE) and expanded PTFE (ePTFE) along with Inorganic Fiber Gaskets (IFG) at different temperatures. The Universal Gasket Rig (UGR) is utilized to perform the convoluted analysis of creep-thermal ratcheting coupling at different stress levels on selected gasket materials. The actual cumulative damage due to thermal ratcheting is separable into upper and lower bound regions indicating the highest and lowest of ratcheting temperature regions. The effect of thermal ratcheting tends to saturate after 12 and 15 cycles for expanded PTFE and fiber gaskets while no saturation is observed with virgin PTFE gaskets even after 20 thermal cycles. IFG and virgin PTFE gaskets exhibited similar percentage of thickness reduction under different applied stresses while expanded PTFE showed a maximum reduction under the least applied stress. The influences of temperature and load are manifested in the compressive creep property which in turn dictates the magnitude of cumulative damage due to thermal ratcheting. Lastly, the impact of creep and thermal ratcheting on the Coefficient of Thermal Expansion (CTE) is evident as the magnitude of CTE upsurges with the extent of induced damage.
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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 | 0.000 | 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) | 0.001 | 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 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".