Coupled Computational Fluid Dynamics and Heat Transfer Modeling of the Effects of Wind Speed and Direction on Temperature Increase of an Ice-Covered FRP Live-Line Tool
Bibliographic record
Abstract
A coupled computational fluid dynamics (CFD) and heat-transfer model for an ice-covered fiberglass-reinforced plastic (FRP) hot stick, elaborated in a previous study, could well explain why the flow of partial-discharge current could be sufficient to raise the temperature of an iced pollution layer just below freezing, where the cold-fog flashover mechanism prevails. However, the ice-covered hot stick was modeled as a solid “ice rod” having an equivalent cross section of ice, meaning that the exposed ice surface is smaller in the model compared to reality. In addition, the simulations were performed for a relatively low wind speed of 1 m/s, while average wind speeds of 6.1–14.4 m/s were reported for the two Manitoba flashovers. Both of these problems are addressed in this paper to deal with the site incident conditions. The ice cover is considered as a thin layer having a thickness of 1 mm on the FRP hot stick. The effects of wind speeds of 0.1–15 m/s and wind direction as parallel and perpendicular to the ice-covered FRP hot stick are studied. This paper also presents experimental investigations on the most reliable reproduction of four separate FRP hot stick flashover incidents in Canada achieved at CIGELE laboratories.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".