Impact of Improving Design Factor over 0.72 on the Safety and Reliability of Gas Pipelines and Feasibility Justification
Bibliographic record
Abstract
Many years experience of the operation of high stress (>72% specified minimum yield strength,SMYS) gas pipelines and statistical analysis results of pipeline incidents showed that the operating pipelines at stress levels over 72% SMYS have not presented problems in USA and Canada,and design factor does not control incidents or the safety of pipelines.Enhancing pipeline safety management level is most important for decreasing incident rate.The application history of higher design factors in the U.S and Canada was reviewed.And the effect of higher factors to the critical flaw size,puncture resistance,change of reliability with time,risk level and the arrest toughness requirements of pipeline were analyzed here.The comparison of pipeline failure rates and risk levels between two design factors (0.72 and 0.8) has shown that a change in design factor from 0.72 to 0.8 would bring little effect on failure rates and risk levels.On the basis of the analysis result,the application feasibility of design factor of 0.8 in China was discussed and the related suggestions were proposed.When an operator wishes to apply design factor 0.8 to gas pipeline,the following process is recommended: stress level of line pipe hydro test should be up to 100% SMYS,reliability and risk assessment at the design feasibility or conceptual stage should be conducted,Charpy impact energy should meet the need of pipeline crack arrest;and establish and execute risk based integrity management plan.The technology of pipeline steel metallurgy,line pipe fabrication and pipeline construction,and line pipe quality control level in China achieved tremendous progresses,and line pipe product standards and property indexes have come up to international advanced level.Furthermore,pipeline safety management has improved greatly in China.Consequently,the research for the feasibility of application of design factor of 0.8 in China has fundamental basis.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".