Ratcheting in pressurized pipes and equipment: A review on affecting parameters, modelling, safety codes, and challenges
Bibliographic record
Abstract
Abstract The current study has attempted to comprehensively review ratcheting response of materials involving various influential parameters such as loading spectra, thermal cycles, stress levels, stress raisers, strain rate, and visco‐plasticity and material types with a focus on pressurized pipes and equipment. The mechanism of deformation, types, and the rate of progress over stages of ratcheting were discussed. Safety design codes and procedures were highlighted for reliable design of pressurized pipes against progressive ratcheting and to safeguard the system against catastrophic failure at which both mechanical and thermal ratcheting were integrated. Boundaries and demarcation of ratcheting‐shakedown zones developed based on Bree's diagram were employed to assess plastic deformation accumulation over stress cycles. Shakedown and ratcheting boundaries were discussed through methods developed on the basis of Melan's theorem over last few decades. Ratcheting response of materials was reviewed through descriptions of parametric models and kinematic hardening rules involving various variables and parameters. Interaction of ratcheting with creep and low‐cycle fatigue has promoted progressive damage in materials. Pressurized pipes subjected to thermal cycles and external bending cycles were evaluated by numerical solutions along with kinematic hardening rules to assess ratcheting over stress cycles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".