The concurrent ratcheting and stiffness degradation-based damage variable in SA508 steel samples undergoing stress cycles at room and elevated temperatures
Bibliographic record
Abstract
Abstract This study evaluates the interaction between ratcheting and stiffness degradation in SA508 steel samples at various operating temperatures using a combined isotropic-kinematic hardening framework. The Ahmadzadeh-Varvani (A-V) kinematic hardening rule, along with the isotropic hardening description by Lee-Zavrel, was employed to respectively translate and expand yield surfaces as the loading level exceeded the yield limit. To address the accumulation of plastic strain at elevated temperatures, the dynamic strain aging phenomenon was introduced through an exponential function into the dynamic recovery term of the A-V model. The evolution of the yield surfaces and materials yield strength was found substantial within a temperature range of 500–778 K where the DSA effect was dominant. A damage variable was defined through stiffness degradation as stress cycles proceeded. The continuum damage mechanics variable was then adapted into the constitutive equations and the hardening framework. The A-V kinematic hardening rule held the damage term in two distinct methods (i) as a multiplier to the linear hardening portion of the A-V model, and (ii) as a multiplier to both linear hardening and dynamic recovery terms. The former adaptation of the damage term verified that the foremost influence of damage was achieved when both linear and non-linear portions of the hardening framework were involved. This resulted in closer agreement of the predicted ratcheting values with those measured. The deviation of the predicted and measured values dropped to 11%. For the latter adaptation, the deviation of predicted ratcheting from experimental was found twice.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".