Application of 3D Constraint-Based Fracture Mechanics for the Determination of R-Curves of Thermal Aged 16MND5 Steel
Bibliographic record
Abstract
Abstract In the current work, the comprehensive combined effects of thermal aging, and three-dimensional (3D) in-plane and out-of-plane constraints on the fracture resistance curves (J-R) of reactor pressure vessel (RPV) steel, 16MND5 were investigated. First, the fracture experiments of RPV steel with different thermal aging durations using variously sized clamped single edge notched tension (SENT) specimens, were summarized. Then, 3D finite element method (FEM) was used to determine the constraint parameters for the tested specimens. From the analysis results, it was shown that the normalized T11, and the independent portion of T33 can accurately quantify the in-plane and out-of-plane constraint levels, respectively. Next, the in-plane and out-of-plane constraint-quantified R-curves were developed. Results showed that the effects of thermal aging, in-plane and out of-plane constraints on fracture toughness present significant interactions. The specific characteristics observed are: the influence of constraints on fracture toughness decreases with the increase of thermal aging duration, especially the in-plane constraint effect; the effect of thermal aging on fracture behavior is more significant under low constraint conditions. Further, the current in-plane and out-of-plane constraint dependent R-curves can easily reproduce all tested R-curves determined by clamped SENT specimens. It was also shown that they can accurately predict R-curves of pin-loaded SENT specimens or real RPV cracked structures under different thermal aging durations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".