An empirical model of the kinetics of hydrogen-induced cracking in pipeline steel, using statistical distribution models and considering microstructural characteristics and hydrogen diffusion parameters
Bibliographic record
Abstract
This study proposes an empirical model to predict the kinetics of hydrogen-induced cracking (HIC) growth rate in pipeline steels based on experimentally measured hydrogen diffusion parameters and spatial distribution of microstructural features previously identified to have a role on HIC kinetics. In the experimental work, the HIC was induced by electrochemical cathodic charging and the crack growth was monitored by ultrasonic inspection. Optical and scanning electron microscopy were used to determine the spatial distribution parameters of non-metallic inclusions, and the ferrite grain and second phase characteristics. The hydrogen microprint technique used to visualize hydrogen diffusion path in the microstructure and the hydrogen diffusion parameters were determined by hydrogen permeation tests. Results show that NMI shape affects HIC nucleation sites, using student's t-distribution, while ferrite grain characteristics affect HIC growth rates, with X70-2 and X56 steel plates recorded highest HIC growth rate. The Log-Normal distribution model, supported by statistical analysis, effectively predicts HIC growth rates compared with Weibull and Gamma distribution models. • Shape of non-metallic inclusions affects HIC nucleation sites. • Ferrite grains mainly affect the HIC growth rate. • Deformed, recrystallized, and {100}-oriented ferrite grains increase HIC growth rate. • Steels with more hydrogen solubility and density traps show higher HIC growth. • Higher volume fraction of pearlite does not correspond to an increased HIC growth rate. • Bainite is less susceptible to HIC than blocky martensite/austenite islands. • Two-degree Log-Normal distribution model more accurately predict HIC growth rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".