A generalized frequency separation–strain energy damage function model for low cycle fatigue–creep life prediction
Bibliographic record
Abstract
ABSTRACT Fatigue–creep interaction is a key factor for the failures of many engineering components and structures under high temperature and cyclic loading. These fatigue–creep life prediction issues are significant in selection, design and safety assessments of those components. Based on the frequency‐modified Manson–Coffin equation and Ostergren's model, a new model for high temperature low cycle fatigue (HTLCF), a generalized frequency separation–strain energy damage function model is developed. The approach used in this model to reflect the effects of time‐dependent damaging mechanisms on HTLCF life is different from those used in all the earlier models. A new strain energy damage function is used to reduce the difference between the approximate strain energy and real strain energy absorbed during the damage process. This proposed model can describe the effects of different time‐dependent damaging mechanisms on HTLCF life more accurately than others. Comparing traditional frequency separation technique (FS) and strain energy frequency‐modified approach (SEFS), the proposed model is widely applicable and more precise in predicting the life of fatigue–creep interaction. Experimental data from existing literature are used to demonstrate the feasibility and applicability of the proposed model. A good agreement is found between the predicted results and experimental data.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".