Hydrogen and hydride induced stress localization in single phase HCP and dual phase HCP-BCC alloys
Bibliographic record
Abstract
• A crystal plasticity finite element (CPFE) model is developed to incorporate the effects of hydrogen induced lattice expansion and phase-dependent hydrogen partitioning. • Results from electron backscatter diffraction (EBSD) measurements are compared with those from CPFE modeling. • Nonuniform hydrogen partitioning between HCP and BCC phases, and hydrogen-induced lattice expansion can lead to significant stress localization in dual-phase HCP-BCC alloys. • The CPFE model correctly predicts the location of hydrides and their corresponding parent α-grains. Hydrogen partitioning and hydride-induced stress localization are important factors in the degradation of dual-phase alloys. This study investigates these mechanisms by developing a crystal plasticity finite element (CPFE) model that incorporates the two-way interaction between stress and hydrogen concentration. The model considers the effects of hydrogen-induced lattice expansion (HILE), phase-dependent hydrogen partitioning, and the transformation strain induced by hydride precipitation. Using this model, the impact of hydrogen on stress distribution and hydride precipitation is examined both in single and dual-phase zirconium alloys with hexagonal close-packed (HCP) and body-centered cubic (BCC) crystals. The results of the model for hydride precipitation are compared with those measured by high-spatial resolution electron backscatter diffraction (EBSD). The findings reveal that HILE effects are more pronounced in dual-phase HCP-BCC alloys due to partitioning of hydrogen between phases. The nonuniform distribution of hydrogen atoms leads to stress localization, which creates favorable conditions for hydride nucleation, particularly near the HCP-BCC interfaces. It is shown that the proposed numerical framework can identify which one of the neighboring HCP grains is the corresponding parent grain of an intergranular hydride.
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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 | 0.000 | 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) | 0.000 | 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".