A Framework for Estimating Burst Test Fracture Toughness for Zr-2.5Nb Pressure Tubes Using Data From Small Specimen Tests
Bibliographic record
Abstract
Abstract Zr-2.5Nb pressure tubes are primary pressure boundaries in a CANDU2 reactor. Design of pressure tube dimensions allows testing of a pressure tube section at its full size in the laboratory. Burst tests, i.e., internally pressuring pressure tube sections containing axial through-wall cracks till burst, have been used to provide test data of fracture toughness for pressure tubes with axial flaws. The advantage of measuring fracture toughness from burst tests is that measured toughness values are directly applicable to operating pressure tubes. Burst tests, however, are costly and consume considerable amount of material. Only a small number of burst tests can be performed in practice. There is strong motivation to estimate burst test fracture toughness using data from small specimen tests. The estimated burst test fracture toughness can fill the gap in the measured burst test toughness data, as well as provide information on material variability and data scatter. The technical challenge for estimating burst test toughness is that the estimated burst test toughness using data from low cost, small specimen tests must be reliable and representative of burst test specimen behavior with high confidence. A framework for accurately estimating burst test toughness using data from curved compact tests has been under development and is described in this paper. Aspects of technical basis and current status of developing analytical procedures for systematically estimating burst test toughness are presented.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".