Predicting crack initiation under the effects of ingot defects in P92 pipes based on a ductile fracture model
Bibliographic record
Abstract
Abstract The ingot defects of P92 martensitic steel could induce crack initiation during the critical normalization-tempering treatment, which deteriorates the properties of P92 pipe used in ultra-supercritical (USC) power plants greatly. Thus, this study conducted numerical simulation based on a modified Johnson-Cook fracture model to thoroughly explore the effects of ingot defects on crack initiation in P92 pipes under common cooling approaches. Tensile tests of smooth and notched P92 steel specimens were executed at wide range of temperatures and strain rates to assist in calibrating the parameters for Johnson-Cook constitutive and fracture models. The fracture criterion for P92 steel as a function of temperatures, strain rates, and stress states simultaneously was thus put forward. Based on calibrated fracture model, validation simulation and experiment were conducted with small P92 CT specimens, showing good consistency between predictions and experiments. Subsequently, cooling simulations of a large P92 pipe with four types of defects placed at various positions of the pipe were performed. Largest strain concentrations were observed around the tips of radial pre-cracks followed by axial pre-crack, void, and inclusion at all positions and cooling rates. Only radial pre-cracks located at inside and outside walls were predicted to initiate under water-spray cooling. Other pre-defects under both air and water-spray cooling pose no risk of crack initiation. The direction of the pre-crack proves to be a crucial factor inducing crack initiation.
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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 | 0.000 |
| 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 | 0.001 | 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".