The Effects of Pressure Fluctuations on Hydrogen Embrittlement in Pipeline Steels
Bibliographic record
Abstract
Hydrogen embrittlement is one of the most severe steel degradation mechanisms. Using hydrogen enhanced decohesion (HEDE) and hydrogen enhanced local plasticity (HELP), we can predict if more hydrogen atoms will accumulate into the plastic zone, enhancing the hydrogen embrittlement and the crack growth rate. In the current study, a relationship has been proposed between operations of pipeline steels and hydrogen accumulation to quantify the effects of hydrogen embrittlement. The study find that hydrogen accumulation rate is proportional to stress intensity and inversely proportional to temperature; hence, higher stress intensity and lower temperature will enhance hydrogen accumulation and crack propagation. Hydrogen potential, diffusivity, hydrostatic stress near the crack tip, and the critical loading frequency have been considered in the new model to predict crack propagation rates in pipeline steels. The predicted values are compared with experimental results of X-65 steel in two near-neutral pH solutions to verify the model. This hydrogen diffusion model helps show former neglected hazard operations such as minor cycles, and offers an easier way to optimize operations that will prolong the life of pipeline steels.
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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 | 0.001 |
| 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) | 0.000 | 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 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".