Experimental Investigation of Dynamic Force on The Performance of Wet Snow Shedding
Bibliographic record
Abstract
Wet snow can create serious electrical and mechanical problems to power transmission networks due to its high adherence to materials. In this study, both adherence of wet snow to cables and wet snow shedding following the application of periodic loads were investigated in small-scale experiments. Wet snow was obtained from fresh dry snow collected from the top layer of snow accumulated outdoors. The collected dry snow was exposed to warm air for periods of time long enough to obtain the desired liquid water content (LWC) values. Wet snow sleeves were manually prepared after being compressed around the cable. The room temperature, snow sleeve length, cable diameter, and cable length were kept constant for each experiment. Dynamic load on the cable was simulated by a periodic excitation applied at one end of the cable, while the other end was fixed. Since fading adhesion is a condition for snow shedding, and since LWC is a critical factor influencing adhesion, the effects of excitation frequency and amplitude were investigated for different LWC values. LWC was examined for values from 10 to 40%. When free water began to increase, snow at this stage was wet and it adhered stronger to the cable than low LWC, where there is no enough water to stick on the cable or high LWC where snow is transparent. The LWC range was found experimentally between 20 to 30% (Hefny R. et al., 2009). Different snow shedding scenarios were studied, for which the shedding sequence and acceleration were observed at the excitation point, also cable tension at the fixed end and vertical displacement at the mid-point of the span were measured.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".