A cost-effective model for synergistic effects of microstructure and crystallographic texture on hydrogen-induced crack growth and corrosion rates in pipeline steels
Bibliographic record
Abstract
This study proposes a Cost-Effective model based on microstructure, crystallographic texture, and hydrogen (H) diffusion to evaluate H-damage in pipeline steels. H-crack growth and corrosion rates, measured using ultrasonic inspection and a Gamry electrochemical setup, were correlated with microstructure and texture. Results show that smaller ferrite grain size, lower density of ∑ 3 co-incidence site lattice boundaries (CSLB), higher densities of geometrically necessary boundaries (GNB) and random high-angle grain boundaries (RHAGB), and higher overall stored energy (E Ave ) in texture fibers increase H-trap sites and reduce effective H-diffusivity, contributing to higher H-crack growth rates. Conversely, these same factors enhance corrosion resistance by improving passivation. Secondary phases have a detrimental effect on H-crack growth and corrosion resistance, varying with size, continuity, and volume fraction of phases. The proposed model, using hyperparameter tuning, quantifies the synergistic effects of microstructure, texture, and H-diffusion on H-damage and highlights the role of ferrite grain size in mitigating H-damage in pipeline steels. Finally, finite element (FE) analysis of grain structures provided supporting observations. • A proposed Cost-Effective model quantifies the synergistic effects of microstructure and texture. • Ferrite grain size is the dominant parameter for evaluating the performance of pipeline steel against H-damage. • Smaller ferrite grain size and misorientation angles >35° significantly increase elastic strain energy density. • Lower density of ∑ 3 CSLB, higher density of dislocations, RHAGB, and E Ave increase H-crack growth but improve corrosion resistance. • Increased volume fractions of γ- and ε-fibers reduce H-crack growth.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".