Fretting fatigue analysis of aluminium conductor wires near the suspension clamp: Metallurgical and fracture mechanics analysis
Bibliographic record
Abstract
Fretting fatigue is the main phenomenon inducing reduction of fatigue strength in overhead transmission conductor lines which are subjected to aeolian vibrations. Using the Bersfort ACSR 48/7 conductor, fatigue tests were conducted under several traction forces and relative vibration amplitudes. By metallographic examinations it has been observed that fretting induces microcracks which are mainly located between the keeper edge (KE) and the last point of the contact (LPC) of the conductor at the mouth of the suspension clamp. Under the fatigue tests conditions, several cross-sections of wires in the external layer of the conductor were instrumented using strain gauges. Wire strains recorded near to the suspension clamp show that the alternating strains were predominantly traction strains near the KE and that bending alternating strains become significant near the LPC. On the basis of fatigue tests results and assuming some simplified hypothesis, stress intensity factors were computed at the tip of cracks. The computed stress intensity factors take into account the combined effect of fretting behaviour at the contact points and both traction and bending stresses. The computed stress intensity factor ranges for low fatigue bending vibration amplitudes show good agreement with the endurance limit of the conductor.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".