Improved approach for predicting weld creep strength factors of ferritic steels
Bibliographic record
Abstract
The cross-weld (CW) creep strength of ferritic steels is typically lower than that for parent metal (PM), and in the past the ratio of CW to PM creep strength (weld strength factor – WSF) was assumed to be limited to ∼80%. For newer Cr steels WSF can be significantly lower for a typical design life of 100 000 h or more. The possibility of low WSF is also accommodated in the current design codes such as EN 13445, but no suggested WSF values are given for guidance. Assuming a too high WSF for such welds obviously results in an unsafe (too long) predicted creep life. Unfortunately, as a further complication the WSF of the newer Cr steels can decrease when the operating temperatures are increased for improved efficiency of future power plants. It is hence important that reliable and sufficiently high values of WSF can be guaranteed. However, there is often much less extensive data on the creep strength of welds than on parent steel, and also the extrapolation to long term values of WSF can add more relative uncertainty than what is expected in extrapolating the long term creep strength of parent steel. Here an improved approach is proposed to predict WSF using the Wilshire creep model to obtain the relationship between the CW creep strength and the corresponding parent material (PM) strength. The Wilshire model directly provides the WSF value for each CW data point, when the expected normalised stress is based on the CW time to rupture at stress and temperature. The corresponding master curve parameters are those for PM, when the PM hot tensile strength is also known. The WSF data points for each CW test can then be fitted for temperature and stress dependence. This approach avoids fitting distortion in WSF, unlike the traditional assessment where a master curve is first obtained for the CW creep strength. As an example, WSF of welded P91 steel at 100 000 h is here predicted in the temperature range of 550–650°C.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".