Ratcheting Responses of Strain Hardening Plasticity Models
Bibliographic record
Abstract
Nuclear reactor components are often subjected to combinations of sustained and cyclic stresses due to the application of internal pressure and transient temperature loads. During the thermal transients, the interaction of such stresses could cause cyclic plastic deformation, which may either lead to shakedown or ratchetting behavior. Design criteria meant to guard against ratcheting are established in Section III of the ASME Code. The criteria are based on perfectly plastic material behavior. However, materials undergo strain hardening upon reaching the yield strength. For elastic-plastic analysis, NB-3228.4 does not contain any guidance on modelling of material hardening, leaving it to the analyst to justify their choice. Although it is widely known that some plasticity models are inadequate for modeling cyclic plasticity, there has been little published study of the effect various plasticity models have on the ratchet boundary. This paper compares the ratchet boundary obtained from bilinear and Chaboche kinematic hardening material models with that obtained from perfect plasticity. The investigation is carried out for simple examples such as the classical Bree problem and a thin plate under biaxial loading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".