Dynamic Cyclic Stress Analysis of Rolling of a Diametrically Loaded Helical Test Specimen
Bibliographic record
Abstract
In a particular nuclear application, separation between structural components is maintained by a helical spring such that the separating load bears across the diameter of the spring coils. Relative motion between the structural components due to changing load and temperature is accommodated by rolling of the spring. This rolling motion while under radial load results in cyclic loading of the spring material. Fatigue analysis of the cyclic loading must take into consideration the material degradation due to the unique operating environment, so testing of ex-service material is required. Standard fatigue test specimens are not possible due to the small dimensions of the spring component, so cyclic stress is applied to the material via a reciprocating rolling motion between two platens. Stress analysis of this arrangement provides the stress range and mean stress necessary for fatigue analysis. A three dimensional finite element dynamic simulation of the test process is applied to a nominal test specimen to determine detailed history of the stress distribution. Numerical challenges are addressed to ensure representative loading conditions are properly applied, and that steady-state response is achieved. Bounding stress ranges are determined, and the implications of the results in fatigue analysis are discussed. It is concluded that response at the ends of the specimens is important in properly evaluating test 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".