Stress Concentration, Stress Intensity and Fatigue Lifetime Calculations for Shrink-Fit Compound Tubes Containing Axial Holes Within the Wall
Bibliographic record
Abstract
Elastic-plastic numerical stress analyses and fatigue lifetime predictions are presented for shrink-fit compound tubes containing multiple, axial holes at the interface between inner and outer tube. The holes, which are semi-circular, are introduced initially as periodic notches on the outer surface of the inner tube and an outer plain tube is heated, slid over the inner tube and allowed to cool to achieve the shrink-fit. Residual stresses resulting from interference and operational stress ranges arising from cyclic bore pressurization are calculated and fatigue lifetimes are predicted. Two potentially critical locations for fatigue failure are identified as the bore and the notch root. The predicted lifetimes are compared with earlier work on a similar overall geometry subjected to autofrettage. The critical location is shown to be at the bore and a clear improvement in overall fatigue lifetime is demonstrated for the shrink-fit tube compared with the autofrettaged tube. As the interface radius is reduced, there is a general reduction in the ratio of fatigue stress range at the bore to that at the hole and the possibility of the critical location moving to the notch root. A normalized presentation for design purposes is proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".