Bibliographic record
Abstract
In this work is presented the development of generic models that emulates the behavior of finite element models under cyclic loads, with the probabilistic representation based on samplings of base-model data for a variety of test cases. The base-model is a pipe with a notch subjected to pressure loading translated into hoop stress and the thermal loading is applied as a cyclic load through the pipe thickness. The probabilistic method takes variations of the nonlinear material properties, loading conditions, and geometrical dimensions, whereas the response variables are defined in terms of stress intensity for the static analyses, and the total accumulated strain as well as the strain ranges translated into the number of allowable load cycles by using the Manson’s common slope method define the response variables for the nonlinear calculations. Bree diagram converted into the Interaction Diagram is used to correlate the results of the nonlinear cyclic analyses and the ASME Code limits for primary and secondary loads from linear elastic analyses, whereas the definition of the shakedown towards of the steady cycle is identified in terms of the local and global components of strain. Furthermore, the Bayesian statistics expands the results of the nonlinear cyclic analysis by combining the interpretations of statistical results to scenarios either not accessible by the frequentist statistics or better served by complex stochastic models.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".