A new modeling framework for fatigue damage of structural components under complex random spectrum
Bibliographic record
Abstract
Time and frequency domains-based fatigue damage prediction approaches have been developed over past decades to predict fatigue performance of mechanical structures subjected to random loads. Frequency domain approaches are increasingly being adapted to provide fatigue assessment of mechanical components subjected to random loads due to computational efficiency and cost savings. Current frequency domain damage models only deal with stationary random loadings where Power Spectral Density (PSD) of random loadings does not change in time. However, many machine components, such as jet engines and tracked vehicles are subjected to evolutionary PSD i.e. random-on-random loadings under real service loads. A new fatigue damage modeling framework is proposed to predict fatigue damage of structures under complex evolutionary PSD where the topology of PSD function changes with time. The proposed modeling approach is based on the underlying concept that the evolutionary PSD response of a structure can be decomposed into a finite number of discrete PSDs. Each PSD can be split into narrow frequency bands so that each of narrowbands can be associated with Rayleigh distribution of stress cycles. Fatigue damage can then be predicted by summing up damages for each individual band and each discrete PSD function on the basis of a damage accumulation rule. The proposed modeling approach is numerically and experimentally validated by a finite element method and experiments using three simplified structures made of 5052-H32 aluminum alloy. The proposed approach provides a more efficient and accurate modeling technique, and account for complex random loadings of structural components.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".